Making trade-offs between rebuild scanning and failing memory device flexibility

ABSTRACT

A dispersed storage network (DSN) includes receiving a slice access request including a slice name, identifying a sub-range of a DSN address range based on the slice name, identifying a memory device of a group of memory devices associated with the sub-range utilizing a decentralized agreement function based on the slice name, facilitating a slice access request with the identified memory device. For rebuilding a slice, a method includes detecting a storage error, identifying a sub-range of the DSN address range, facilitating rebuilding of the identified sub-range to produce rebuilt encoded data slices, updating location weights of the group of memory devices based on the detected storage error, for each rebuilt encoded data slice, identifying a corresponding memory device of the group of memory devices for storage of the rebuilt encoded data slice utilizing the decentralized agreement function and the updated location weights, and storing the rebuilt encoded data slice.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 120 as a continuation-in-part of U.S. Utility applicationSer. No. 14/707,999, entitled “ACCESSING DATA WHILE MIGRATING STORAGE OFTHE DATA,” filed May 8, 2015, which claims priority pursuant to 35U.S.C. § 119(e) to U.S. Provisional Application No. 62/019,126, entitled“SELECTING STORAGE RESOURCES OF A DISPERSED STORAGE NETWORK”, filed Jun.30, 2014, both of which are hereby incorporated herein by reference intheir entirety and made part of the present U.S. Utility patentapplication for all purposes.

BACKGROUND Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data using a decentralizedagreement protocol.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

SUMMARY

According to an embodiment of the present invention, a decentralizedagreement protocol is utilized to rank storage locations in a dispersedstorage network (DSN) for data access operations. In response toreceiving a DSN access request, within a DS unit, sub-ranges of the DSunits total owned namespace range may be subdivided into some numbergreater than 1, but less than the number of memory devices in that DSunit. The resulting sub-ranges are assigned to groups of memory devices.When a slice is routed (by its name) to a particular group of memorydevices, a DAP is then used to determine which memory device in thatgroup will receive the slice. Should any memory device fail in a group,then only the namespace range owned by that group is impacted and hasdiminished health. To recover health, rebuild scanning and rebuildingneed only be applied across that sub-range.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present disclosure;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present disclosure;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present disclosure;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent disclosure;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present disclosure;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present disclosure;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present disclosure;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent disclosure;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present disclosure;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present disclosure;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present disclosure;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present disclosure;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent disclosure;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present disclosure;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present disclosure;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present disclosure;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent disclosure;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present disclosure;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present disclosure;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present disclosure;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present disclosure;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentdisclosure;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present disclosure;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentdisclosure;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present disclosure;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentdisclosure;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentdisclosure;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent disclosure;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present disclosure;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentdisclosure;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentdisclosure;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present disclosure;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present disclosure;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentdisclosure;

FIG. 40A is a schematic block diagram of an embodiment of adecentralized agreement module in accordance with the presentdisclosure;

FIG. 40B is a flowchart illustrating an example of selecting theresource in accordance with the present disclosure;

FIG. 40C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present disclosure;

FIG. 40D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory in accordance with the present disclosure;

FIG. 41A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentdisclosure;

FIG. 41B is a flowchart illustrating an example of accessing andrebuilding encoded data slices in accordance with the presentdisclosure;

FIG. 42A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentdisclosure;

FIG. 42B is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentdisclosure;

FIG. 42C is a flowchart illustrating an example of selecting storageresources in accordance with the present disclosure;

FIG. 43A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentdisclosure;

FIG. 43B is a flowchart illustrating another example of selectingstorage resources slices in accordance with the present disclosure;

FIG. 44A is a schematic block diagram of another embodiment of adistributed storage and task (DST) execution (EX) unit in accordancewith the present disclosure;

FIG. 44B is a flowchart illustrating an example of de-marking encodeddata slices in accordance with the present disclosure;

FIGS. 45A-45E are a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentdisclosure;

FIG. 45F is a flowchart illustrating an example of accessing data whilemigrating storage of the data in accordance with the present disclosure;

FIG. 46A is a schematic block diagram of another embodiment of adistributed storage and task network (DSTN) in accordance with thepresent disclosure;

FIG. 46B is a flowchart illustrating an example of selecting taskexecution resources in accordance with the present disclosure;

FIG. 47A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentdisclosure;

FIG. 47B is a flowchart illustrating an example of updating storage unitconfiguration information in accordance with the present disclosure;

FIG. 48A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentdisclosure;

FIG. 48B is a flowchart illustrating another example of migrating slicesin accordance with the present disclosure;

FIG. 49A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentdisclosure; and

FIG. 49B is a flowchart illustrating another example of migrating slicesin accordance with the present disclosure.

DETAILED DESCRIPTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a computing device 12 and/or acomputing device 14, a distributed storage and/or task (DST) processingunit 16, a distributed storage and/or task network (DSTN) managing unit18, a DST integrity processing unit 20, and a distributed storage and/ortask network (DSTN) module 22. The components of the distributedcomputing system 10 are coupled via a network 24, which may include oneor more wireless and/or wire lined communication systems; one or moreprivate intranet systems and/or public internet systems; and/or one ormore local area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the computing devices 12-14, the DST processing unit 16, theDSTN managing unit 18, and the DST integrity processing unit 20 includea computing core 26 and may be a portable computing device and/or afixed computing device. A portable computing device may be a socialnetworking device, a gaming device, a cell phone, a smart phone, apersonal digital assistant, a digital music player, a digital videoplayer, a laptop computer, a handheld computer, a tablet, a video gamecontroller, and/or any other portable device that includes a computingcore. A fixed computing device may be a personal computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.User device 12 and DST processing unit 16 are configured to include aDST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between computing device 14 and the DSTprocessing unit 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing device 12 and the DSTN module 22 and between theDST processing unit 16 and the DSTN module 22. As yet another example,interface 33 supports a communication link for each of the DSTN managingunit 18 and DST integrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general, and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a computing device 12-14. For instance,if a second type of computing device 14 has data 40 to store in the DSTNmodule 22, it sends the data 40 to the DST processing unit 16 via itsinterface 30. The interface 30 functions to mimic a conventionaloperating system (OS) file system interface (e.g., network file system(NFS), flash file system (FFS), disk file system (DFS), file transferprotocol (FTP), web-based distributed authoring and versioning (WebDAV),etc.) and/or a block memory interface (e.g., small computer systeminterface (SCSI), internet small computer system interface (iSCSI),etc.). In addition, the interface 30 may attach a user identificationcode (ID) to the data 40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a computingdevice 12-14 individually or as part of a group of computing devices.For example, the DSTN managing unit 18 coordinates creation of a vault(e.g., a virtual memory block) within memory of the DSTN module 22 for acomputing device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The DSTN managing unit 18 may facilitate storage of DSerror encoding parameters for each vault of a plurality of vaults byupdating registry information for the distributed computing system 10.The facilitating includes storing updated registry information in one ormore of the DSTN module 22, the computing device 12, the DST processingunit 16, and the DST integrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by acomputing device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials forcomputing devices, adding/deleting components (e.g., computing devices,DST execution units, and/or DST processing units) from the distributedcomputing system 10, and/or establishing authentication credentials forDST execution units 36. Network administration includes monitoringdevices and/or units for failures, maintaining vault information,determining device and/or unit activation status, determining deviceand/or unit loading, and/or determining any other system level operationthat affects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a computing device 12-14individually or as part of a group of computing devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials forcomputing devices, adding/deleting components (e.g., computing devices,DST execution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of computingdevice 14 has a task request 38 for execution by the DSTN module 22, itsends the task request 38 to the DST processing unit 16 via itsinterface 30. An example of DST execution on stored data will bediscussed in greater detail with reference to FIGS. 27-39. With respectto the DST management, it is substantially similar to the DST managementto support distributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (TO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the computing device 14 of FIG. 1.Further note that the IO device interface module 62 and/or the memoryinterface modules may be collectively or individually referred to as IOports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in computing device 14 and/or inDST processing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terabytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terabytes) into 100,000 data segments, each being 1 Gigabyte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the grouping selector modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The grouping selector module 114 outputs the slicegroupings 96 to the corresponding DST execution units 36 via the network24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, where in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phrase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of encodeddata segments 156 for a given data partition, the slicing module outputsa plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7. In this example, data segment 1 includes 3 rows with each rowbeing treated as one word for encoding. As such, data segment 1 includesthree words for encoding: word 1 including data blocks d1 and d2, word 2including data blocks d16 and d17, and word 3 including data blocks d31and d32. Each of data segments 2-7 includes three words where each wordincludes two data blocks. Data segment 8 includes three words where eachword includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES_1 andES_2) of the first set of encoded data slices include error correctiondata based on the first-third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slicing of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the second set of encoded data slices includes errorcorrection data based on the first-third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selector module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selector module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selector module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selector module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DSTexecution unit #4, which includes fourth encoded slices of each of thesets of encoded slices. As such, the fourth DST execution unit receivesencoded data slices corresponding to first error encoding information(e.g., encoded data slices of error coding (EC) data). The groupingselector module 114 further creates a fifth slice grouping for DSTexecution unit #5, which includes fifth encoded slices of each of thesets of encoded slices. As such, the fifth DST execution unit receivesencoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_3) is sent to the fourth DST execution unit; the fourth slice groupingof the second data partition (e.g., slice group 2_4, which includesfirst error coding information) is sent to the fifth DST execution unit;and the fifth slice grouping of the second data partition (e.g., slicegroup 2_5, which includes second error coding information) is sent tothe first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9. The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or anyother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identify other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 in thememory 88. For example, when the partial task 98 includes a retrievalrequest, the controller 86 outputs the memory control 174 to the memory88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step of executing the task in accordance with the taskcontrol information 176, the DT execution module 90 retrieves theencoded slices from memory 88. The DT execution module 90 thenreconstructs contiguous data blocks of a data partition. As shown forthis example, reconstructed contiguous data blocks of data partition 1include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbound DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieved slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9.Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8, an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23.The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24.

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupingselector module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 218 of thedata segments into pillars of slices 216. The number of pillarscorresponds to the pillar width of the DS error encoding parameters. Inthis example, the distributed task control module 118 facilitates thestorage request.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21. TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selector module organizes the sets of encoded dataslices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selector module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selector module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Solomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a computing device orof a DST processing unit issues a DST request to the DSTN module. TheDST request may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19.

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39. In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a computing device and the second may beassociated with a DST processing unit or a high priority computingdevice (e.g., high priority clearance user, system administrator, etc.).Each DST client module includes a list of stored data 234 and a list oftasks codes 236. The list of stored data 234 includes one or moreentries of data identifying information, where each entry identifiesdata stored in the DSTN module 22. The data identifying information(e.g., data ID) includes one or more of a data file name, a data filedirectory listing, DSTN addressing information of the data, a dataobject identifier, etc. The list of tasks 236 includes one or moreentries of task code identifying information, when each entry identifiestask codes stored in the DSTN module 22. The task code identifyinginformation (e.g., task ID) includes one or more of a task file name, atask file directory listing, DSTN addressing information of the task,another type of identifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the computing device associated with the first DST client modulehas fewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the computing device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the computing device associated with the first DSTclient module has selected fewer data and/or fewer tasks than theoperator of the device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the computing devicethat contains the first DST client module, or may be within the DSTNmodule 22.

Regardless of the task distribution module's location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28. The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task ⇔ sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few Terabytes or more),addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1;and SLC_1. In this example, the addressing information may be a virtualaddress corresponding to the virtual address of the first storage word(e.g., one or more bytes) of the data and information on how tocalculate the other addresses, may be a range of virtual addresses forthe storage words of the data, physical addresses of the first storageword or the storage words of the data, may be a list of slice names ofthe encoded data slices of the data, etc. The DS parameters may includeidentity of an error encoding scheme, decode threshold/pillar width(e.g., 3/5 for the first data entry), segment security information(e.g., SEG_1), per slice security information (e.g., SLC_1), and/or anyother information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_2_XY, and DSparameters of 3/5; SEG_2; and SLC_2. In this example, the addressinginformation may be a virtual address corresponding to the virtualaddress of the first storage word (e.g., one or more bytes) of the taskand information on how to calculate the other addresses, may be a rangeof virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., 3/5 for the first data entry),segment security information (e.g., SEG_2), per slice securityinformation (e.g., SLC_2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task ⇔ sub-task mapping information table 246 includes a task field256 and a sub-task field 258. The task field 256 identifies a taskstored in the memory of a distributed storage and task network (DSTN)module and the corresponding sub-task fields 258 indicates whether thetask includes sub-tasks and, if so, how many and if any of the sub-tasksare ordered. In this example, the task ⇔ sub-task mapping informationtable 246 includes an entry for each task stored in memory of the DSTNmodule (e.g., task 1 through task k). In particular, this exampleindicates that task 1 includes 7 sub-tasks; task 2 does not includesub-tasks, and task k includes r number of sub-tasks (where r is aninteger greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identifynon-words (non-ordered); task 1_2—identify unique words (non-ordered);task 1_3—translate (non-ordered); task 1_4—translate back (ordered aftertask 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task1_6—determine non-word translation errors (ordered after task 1_5 and1_1); and task 1_7—determine correct translations (ordered after 1_5 and1_2). The sub-task further indicates whether they are an ordered task(i.e., are dependent on the outcome of another task) or non-order (i.e.,are independent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_1 translate; andtask 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases 288. The translated data 282 istranslated back 308 (e.g., sub-task 1_4) into the language of theoriginal data to produce re-translated data 284. These two tasks aredependent on the translate task (e.g., task 1_3) and thus must beordered after the translation task, which may be in a pipelined orderingor a serial ordering. The re-translated data 284 is then compared 310with the original data 92 to find words and/or phrases that did nottranslate (one way and/or the other) properly to produce a list ofincorrectly translated words 294. As such, the comparing task (e.g.,sub-task 1_5) 310 is ordered after the translation 306 andre-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30. As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29, the respective data/task has DSparameters of 3/5 for their decode threshold/pillar width; hencespanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30. The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done the by DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and5_1 search for non-words in data partitions 2_1 through 2_z to producetask 1_1 intermediate results (R1-1, which is a list of non-words). Task1_2 (e.g., identify unique words) has similar task execution informationas task 1_1 to produce task 1_2 intermediate results (R1-2, which is thelist of unique words).

Task 1_3 (e.g., translate) includes task execution information as beingnon-ordered (i.e., is independent), having DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 andhaving DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate datapartitions 2_5 through 2_z to produce task 1_3 intermediate results(R1-3, which is the translated data). In this example, the datapartitions are grouped, where different sets of DT execution modulesperform a distributed sub-task (or task) on each data partition group,which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3's intermediate result (e.g., R1-3_1) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1_3 intermediate resultpartitions R1-3_5 through R1-3_z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4'sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5,which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_6 intermediate results (R1-6, which is the list translation errors dueto non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_7 intermediate results (R1-7, which is the list of correctlytranslated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2_z by DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1,4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in datapartitions 2_1 through 2_z to produce task 2 intermediate results (R2,which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3_z) to producetask 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30. In FIG.33, the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32) executes task 1_1 to produce a secondpartial result 102 of non-words found in the second data partition. Thesets of DT execution modules (as per the DST allocation information)perform task 1_1 on the data partitions until the “z” set of DTexecution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results toproduce the first intermediate result (R1-1), which is a list ofnon-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slicegrouping-based DS error encode the first intermediate result (e.g., thelist of non-words). To begin the encoding, the DST client moduledetermines whether the list of non-words is of a sufficient size topartition (e.g., greater than a Terabyte). If yes, it partitions thefirst intermediate result (R1-1) into a plurality of partitions (e.g.,R1-1_1 through R1-1_m). If the first intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results102 of task 1_2 to produce the second intermediate result (R1-2), whichis a list of unique words found in the data 92. The processing module ofDST execution 1 is engaged to aggregate the first through “zth” partialresults of unique words to produce the second intermediate result. Theprocessing module stores the second intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slicegrouping-based DS error encode the second intermediate result (e.g., thelist of non-words). To begin the encoding, the DST client moduledetermines whether the list of unique words is of a sufficient size topartition (e.g., greater than a Terabyte). If yes, it partitions thesecond intermediate result (R1-2) into a plurality of partitions (e.g.,R1-2_1 through R1-2_m). If the second intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2_z). For the data partitions, theallocated set of DT execution modules 90 executes task 1_3 to producepartial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_3 to produce the third intermediate result (R1-3), which istranslated data. The processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of translated data toproduce the third intermediate result. The processing module stores thethird intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slicegrouping-based DS error encode the third intermediate result (e.g.,translated data). To begin the encoding, the DST client modulepartitions the third intermediate result (R1-3) into a plurality ofpartitions (e.g., R1-3_1 through R1-3_y). For each partition of thethird intermediate result, the DST client module uses the DS errorencoding parameters of the data (e.g., DS parameters of data 2, whichincludes 3/5 decode threshold/pillar width ratio) to produce slicegroupings. The slice groupings are stored in the intermediate resultmemory (e.g., allocated memory in the memories of DST execution units2-6 per the DST allocation information).

As is further shown in FIG. 35, the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_4 to produce the fourth intermediate result (R1-4), which isretranslated data. The processing module of DST execution 3 is engagedto aggregate the first through “zth” partial results of retranslateddata to produce the fourth intermediate result. The processing modulestores the fourth intermediate result as non-DS error encoded data inthe scratchpad memory or in another section of memory of DST executionunit 3.

DST execution unit 3 engages its DST client module to slicegrouping-based DS error encode the fourth intermediate result (e.g.,retranslated data). To begin the encoding, the DST client modulepartitions the fourth intermediate result (R1-4) into a plurality ofpartitions (e.g., R1-4_1 through R1-4_z). For each partition of thefourth intermediate result, the DST client module uses the DS errorencoding parameters of the data (e.g., DS parameters of data 2, whichincludes 3/5 decode threshold/pillar width ratio) to produce slicegroupings. The slice groupings are stored in the intermediate resultmemory (e.g., allocated memory in the memories of DST execution units3-7 per the DST allocation information).

In FIG. 36, a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35. To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results oftask 1_5 to produce the fifth intermediate result (R1-5), which is thelist of incorrectly translated words and/or phrases. In particular, theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of the list of incorrectly translatedwords and/or phrases to produce the fifth intermediate result. Theprocessing module stores the fifth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slicegrouping-based DS error encode the fifth intermediate result. To beginthe encoding, the DST client module partitions the fifth intermediateresult (R1-5) into a plurality of partitions (e.g., R1-5_1 throughR1-5_z). For each partition of the fifth intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_6 to produce the sixth intermediate result (R1-6), which is thelist of incorrectly translated words and/or phrases due to non-words. Inparticular, the processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases due to non-words to producethe sixth intermediate result. The processing module stores the sixthintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slicegrouping-based DS error encode the sixth intermediate result. To beginthe encoding, the DST client module partitions the sixth intermediateresult (R1-6) into a plurality of partitions (e.g., R1-6_1 throughR1-6_z). For each partition of the sixth intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task1_7 (e.g., correctly translated words and/or phrases) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_7 to produce the seventh intermediate result (R1-7), which is thelist of correctly translated words and/or phrases. In particular, theprocessing module of DST execution 3 is engaged to aggregate the firstthrough “zth” partial results of the list of correctly translated wordsand/or phrases to produce the seventh intermediate result. Theprocessing module stores the seventh intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slicegrouping-based DS error encode the seventh intermediate result. To beginthe encoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 7 is assigned to process the first through “zth” partial results oftask 2 to produce task 2 intermediate result (R2), which is a list ofspecific words and/or phrases found in the data. The processing moduleof DST execution 7 is engaged to aggregate the first through “zth”partial results of specific words and/or phrases to produce the task 2intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slicegrouping-based DS error encode the task 2 intermediate result. To beginthe encoding, the DST client module determines whether the list ofspecific words and/or phrases is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the task 2intermediate result (R2) into a plurality of partitions (e.g., R2_1through R2_m). If the task 2 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 5 is assigned to process the first through “zth” partial results oftask 3 to produce task 3 intermediate result (R3), which is a list ofspecific translated words and/or phrases found in the translated data.In particular, the processing module of DST execution 5 is engaged toaggregate the first through “zth” partial results of specific translatedwords and/or phrases to produce the task 3 intermediate result. Theprocessing module stores the task 3 intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 7.

DST execution unit 5 engages its DST client module to slicegrouping-based DS error encode the task 3 intermediate result. To beginthe encoding, the DST client module determines whether the list ofspecific translated words and/or phrases is of a sufficient size topartition (e.g., greater than a Terabyte). If yes, it partitions thetask 3 intermediate result (R3) into a plurality of partitions (e.g.,R3_1 through R3_m). If the task 3 intermediate result is not ofsufficient size to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30. In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a schematic block diagram of an embodiment of adecentralized agreement module 350 that includes a set of deterministicfunctions 1-N, a set of normalizing functions 1-N, a set of scoringfunctions 1-N, and a ranking function 352. Each of the deterministicfunction, the normalizing function, the scoring function, and theranking function 352, may be implemented utilizing the processing module84 of FIG. 3. The decentralized agreement module 350 may be implementedutilizing any module and/or unit of a dispersed storage network (DSN).For example, the decentralized agreement module is implemented utilizingthe distributed storage and task (DST) client module 34 of FIG. 1.

The decentralized agreement module 350 functions to receive a rankedscoring information request 354 and to generate ranked scoringinformation 358 based on the ranked scoring information request 354 andother information. The ranked scoring information request 354 includesone or more of an asset identifier (ID) 356 of an asset associated withthe request, an asset type indicator, one or more location identifiersof locations associated with the DSN, one or more corresponding locationweights, and a requesting entity ID. The asset includes any portion ofdata associated with the DSN including one or more asset types includinga data object, a data record, an encoded data slice, a data segment, aset of encoded data slices, and a plurality of sets of encoded dataslices. As such, the asset ID 356 of the asset includes one or more of adata name, a data record identifier, a source name, a slice name, and aplurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations includes one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSTclient module 34 of FIG. 1, a DST processing unit 16 of FIG. 1, a DSTintegrity processing unit 20 of FIG. 1, a DSTN managing unit 18 of FIG.1, a computing device 12 of FIG. 1, and a computing device 14 of FIG. 1.

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule 350 to produce ranked scoring information 358 with regards toselection of a memory device of the set of memory devices for accessinga particular encoded data slice (e.g., where the asset ID includes aslice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule 350 and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module 350 to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module 350and the second requesting entity identifies the first storage pool ofthe plurality of storage pools for retrieving the set of encoded dataslices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350receives the ranked scoring information request 354. Each deterministicfunction performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the asset ID 356 of theranked scoring information request 354 and an associated location ID ofthe set of location IDs to produce an interim result. The deterministicfunction includes at least one of a hashing function, a hash-basedmessage authentication code function, a mask generating function, acyclic redundancy code function, hashing module of a number oflocations, consistent hashing, rendezvous hashing, and a spongefunction. As a specific example, deterministic function 2 appends alocation ID 2 of a storage pool 2 to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 2.

With a set of interim results 1-N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function includes dividing the interim result by a number ofpossible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1-N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. The performing of the scoring functionincludes dividing an associated location weight by a negative log of thenormalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1-N, the ranking function 352 performs a rankingfunction on the set of scores 1-N to generate the ranked scoringinformation 358. The ranking function includes rank ordering each scorewith other scores of the set of scores 1-N, where a highest score isranked first. As such, a location associated with the highest score maybe considered a highest priority location for resource utilization(e.g., accessing, storing, retrieving, etc., the given asset of therequest). Having generated the ranked scoring information 358, thedecentralized agreement module 350 outputs the ranked scoringinformation 358 to the requesting entity.

FIG. 40B is a flowchart illustrating an example of selecting a resource.The method begins or continues at step 360 where a processing module(e.g., of a decentralized agreement module) receives a ranked scoringinformation request from a requesting entity with regards to a set ofcandidate resources. For each candidate resource, the method continuesat step 362 where the processing module performs a deterministicfunction on a location identifier (ID) of the candidate resource and anasset ID of the ranked scoring information request to produce an interimresult. As a specific example, the processing module combines the assetID and the location ID of the candidate resource to produce a combinedvalue and performs a hashing function on the combined value to producethe interim result.

For each interim result, the method continues at step 364 where theprocessing module performs a normalizing function on the interim resultto produce a normalized interim result. As a specific example, theprocessing module obtains a permutation value associated with thedeterministic function (e.g., maximum number of permutations of outputof the deterministic function) and divides the interim result by thepermutation value to produce the normalized interim result (e.g., with avalue between 0 and 1).

For each normalized interim result, the method continues at step 366where the processing module performs a scoring function on thenormalized interim result utilizing a location weight associated withthe candidate resource associated with the interim result to produce ascore of a set of scores. As a specific example, the processing moduledivides the location weight by a negative log of the normalized interimresult to produce the score.

The method continues at step 368 where the processing module rank ordersthe set of scores to produce ranked scoring information (e.g., ranking ahighest value first). The method continues at step 370 where theprocessing module outputs the ranked scoring information to therequesting entity. The requesting entity may utilize the ranked scoringinformation to select one location of a plurality of locations.

FIG. 40C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) that includes the distributed storage and task(DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1, and thedistributed storage and task network (DSTN) module 22 of FIG. 1.Hereafter, the DSTN module 22 may be interchangeably referred to as aDSN memory. The DST processing unit 16 includes a decentralizedagreement module 380 and the DST client module 34 of FIG. 1. Thedecentralized agreement module 380 be implemented utilizing thedecentralized agreement module 350 of FIG. 40A. The DSTN module 22includes a plurality of DST execution (EX) unit pools 1-P. Each DSTexecution unit pool includes a one or more sites 1-S. Each site includesone or more DST execution units 1-N. Each DST execution unit may beassociated with at least one pillar of N pillars associated with aninformation dispersal algorithm (IDA), where a data segment is dispersedstorage error encoded using the IDA to produce one or more sets ofencoded data slices, and where each set includes N encoded data slicesand like encoded data slices (e.g., slice 3's) of two or more sets ofencoded data slices are included in a common pillar (e.g., pillar 3).Each site may not include every pillar and a given pillar may beimplemented at more than one site. Each DST execution unit includes aplurality of memories 1-M. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1. Hereafter, a DSTexecution unit may be referred to interchangeably as a storage unit anda set of DST execution units may be interchangeably referred to as a setof storage units and/or as a storage unit set.

The DSN functions to receive data access requests 382, select resourcesof at least one DST execution unit pool for data access, utilize theselected DST execution unit pool for the data access, and issue a dataaccess response 392 based on the data access. The selecting of theresources includes utilizing a decentralized agreement function of thedecentralized agreement module 380, where a plurality of locations areranked against each other. The selecting may include selecting onestorage pool of the plurality of storage pools, selecting DST executionunits at various sites of the plurality of sites, selecting a memory ofthe plurality of memories for each DST execution unit, and selectingcombinations of memories, DST execution units, sites, pillars, andstorage pools.

In an example of operation, the DST client module 34 receives the dataaccess request 382 from a requesting entity, where the data accessrequest 382 includes at least one of a store data request, a retrievedata request, a delete data request, a data name, and a requestingentity identifier (ID). Having received the data access request 382, theDST client module 34 determines a DSN address associated with the dataaccess request. The DSN address includes at least one of a source name(e.g., including a vault ID and an object number associated with thedata name), a data segment ID, a set of slice names, a plurality of setsof slice names. The determining includes at least one of generating(e.g., for the store data request) and retrieving (e.g., from a DSNdirectory, from a dispersed hierarchical index) based on the data name(e.g., for the retrieve data request).

Having determined the DSN address, the DST client module 34 selects aplurality of resource levels (e.g., DST EX unit pool, site, DSTexecution unit, pillar, memory) associated with the DSTN module 22. Thedetermining may be based on one or more of the data name, the requestingentity ID, a predetermination, a lookup, a DSN performance indicator,and interpreting an error message. For example, the DST client module 34selects the DST execution unit pool as a first resource level and a setof memory devices of a plurality of memory devices as a second resourcelevel based on a system registry lookup for a vault associated with therequesting entity.

Having selected the plurality resource levels, the DST client module 34,for each resource level, issues a ranked scoring information request 384to the decentralized agreement module 380 utilizing the DSN address asan asset ID. The decentralized agreement module 380 performs thedecentralized agreement function based on the asset ID (e.g., the DSNaddress), identifiers of locations of the selected resource levels, andlocation weights of the locations to generate ranked scoring information386.

For each resource level, the DST client module 34 receives correspondingranked scoring information 386. Having received the ranked scoringinformation 386, the DST client module 34 identifies one or moreresources associated with the resource level based on the rank scoringinformation 386. For example, the DST client module 34 identifies a DSTexecution unit pool associated with a highest score and identifies a setof memory devices within DST execution units of the identified DSTexecution unit pool with a highest score.

Having identified the one or more resources, the DST client module 34accesses the DSTN module 22 based on the identified one or moreresources associated with each resource level. For example, the DSTclient module 34 issues resource access requests 388 (e.g., write slicerequests when storing data, read slice requests when recovering data) tothe identified DST execution unit pool, where the resource accessrequests 388 further identify the identified set of memory devices.Having accessed the DSTN module 22, the DST client module 34 receivesresource access responses 390 (e.g., write slice responses, read sliceresponses). The DST client module 34 issues the data access response 392based on the received resource access responses 390. For example, theDST client module 34 decodes received encoded data slices to reproducedata and generates the data access response 392 to include thereproduced data.

FIG. 40D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory. The method begins or continues at step 394where a processing module (e.g., of a distributed storage and task (DST)client module) receives a data access request from a requesting entity.The data access request includes one or more of a storage request, aretrieval request, a requesting entity identifier, and a data identifier(ID). The method continues at step 396 where the processing moduledetermines a DSN address associated with the data access request. Forexample, the processing module generates the DSN address for the storagerequest. As another example, the processing module performs a lookup forthe retrieval request based on the data identifier.

The method continues at step 398 where the processing module selects aplurality resource levels associated with the DSN memory. The selectingmay be based on one or more of a predetermination, a range of weightsassociated with available resources, a resource performance level, and aresource performance requirement level. For each resource level, themethod continues at step 400 where the processing module determinesranked scoring information. For example, the processing module issues aranked scoring information request to a decentralized agreement modulebased on the DSN address and receives corresponding ranked scoringinformation for the resource level, where the decentralized agreementmodule performs a decentralized agreement protocol function on the DSNaddress using the associated resource identifiers and resource weightsfor the resource level to produce the ranked scoring information for theresource level.

For each resource level, the method continues at step 402 where theprocessing module selects one or more resources associated with theresource level based on the ranked scoring information. For example, theprocessing module selects a resource associated with a highest scorewhen one resource is required. As another example, the processing moduleselects a plurality of resources associated with highest scores when aplurality of resources are required.

The method continues at step 404 where the processing module accessesthe DSN memory utilizing the selected one or more resources for each ofthe plurality of resource levels. For example, the processing moduleidentifies network addressing information based on the selectedresources including one or more of a storage unit Internet protocoladdress and a memory device identifier, generates a set of encoded dataslice access requests based on the data access request and the DSNaddress, and sends the set of encoded data slice access requests to theDSN memory utilizing the identified network addressing information.

The method continues at step 406 where the processing module issues adata access response to the requesting entity based on one or moreresource access responses from the DSN memory. For example, theprocessing module issues a data storage status indicator when storingdata. As another example, the processing module generates the dataaccess response to include recovered data when retrieving data.

FIG. 41A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes a set of distributedstorage and task (DST) execution units 1-n and the network 24 of FIG. 1.Each DST execution unit includes a decentralized agreement module 410,the DST client module 34 of FIG. 1, and a plurality of memories 1-60.Each decentralized agreement module 410 may be implemented utilizing thedecentralized agreement module 350 of FIG. 40A. Each memory may beimplemented utilizing the memory 88 of FIG. 3.

The DSN functions to provide access to data stored as a plurality ofsets of encoded data slices in the set of DST execution units and torebuild encoded data slices associated with storage errors. In anexample of operation of accessing the data, a DST client module 34 of aDST execution unit receives a slice access request that includes a slicename of an encoded data slice. Having received the slice name, the DSTclient module 34 identifies a range of DSN addresses of a plurality ofranges of DSN addresses associated with the DST execution unit (e.g.,received, look up), where the range of DSN addresses includes a slicename. For example, each DST execution unit is associated with 10 DSNranges, where each DSN address range is mapped to a subset of theplurality of memories. For instance, a second DSN address range ismapped to memories 7-12 and a tenth DSN address range is mapped tomemories 55-60.

Having identified the range of DSN addresses, the DST client module 34identifies a memory device of a corresponding subset of the plurality ofmemories using a decentralized agreement function based on the slicename and current location weights of each memory device of the subset ofmemory devices. For example, the DST client module 34 issues a rankedscoring information request 414 to the decentralized agreement module410, receives the ranked scoring information 416, and identifies amemory device associated with a highest score.

Having identified the memory device, the DST client module 34facilitates the slice access request with the identified memory device.For example, the DST client module 34 retrieves the encoded data slicefrom the identified memory device and sends the retrieved encoded dataslice to a requesting entity when the slice access request includes aretrieve slice request. As another example, the DST client module 34stores an encoded data slice of the slice access request into theidentified memory device when the slice access request includes a storeslice request.

In an example of operation of the rebuilding, the DST client module 34detects a failed memory device within an associated subset of memorydevices. The detecting includes at least one of interpreting an errormessage, performing a memory test, or interpreting a memory test result.Having detected the failed memory device, the DST client module 34identifies the DSN address range associated with the subset of memorydevices (e.g., a lookup). Having identified the DSN address range, theDST client module 34 facilitates rebuilding for the identified DSNaddress range by accessing the subset of memory devices, issuingrebuilding messages 412 to other DST execution units of the set of DSTexecution units, receiving further rebuilding messages 412 from theother DST execution units, identifying missing encoded data slices ofthe subset of memory devices, and generating rebuilt encoded dataslices.

Having facilitated the rebuilding, the DST client module 34 updateslocation weights of the subset of memory devices based on the failure.For example, the DST client module 34 zeros out a location weight of thefailed memory device and raises location weights of remaining memorydevices of the subset of memory devices that includes the failed memorydevice. Having updated the location weights, the DST client module 34facilitates storage of the rebuilt encoded data slices utilizing thedecentralized agreement function based on corresponding slice names andthe updated location weights of the memory devices of the subset ofmemory devices.

FIG. 41B is a flowchart illustrating an example of accessing andrebuilding encoded data slices. The method begins or continues, whenaccessing an encoded data slice, at step 420 where a processing module(e.g., of a distributed storage and task (DST) client module of astorage unit) receives a slice access request that includes a slicename. The method continues at step 422 where the processing moduleidentifies a sub-range of a DSN address range associated with thestorage unit. For example, the processing module accesses a slice nameto sub-range table. As another example, the processing module performs adeterministic function on the slice name to produce the sub-range.

The method continues at step 424 where the processing module identifiesa memory device of a group of memory devices associated with thesub-range utilizing a decentralized agreement function based on theslice name. For example, the processing module performs thedecentralized agreement function to produce scores for each of thememory devices of a group of memory devices using one or more oflocation weights of each memory device, the slice name, or a memorygroup identifier. The method continues at step 426 where the processingmodule facilitates a slice access request with the identified memorydevice (e.g., store, retrieve, delete, list).

The method begins or continues, when rebuilding, at step 428 where theprocessing module detects a storage error associated with a memorydevice of the group of memory devices. The detecting includes one ormore of receiving an error message, performing a memory device test,interpreting a memory device test result, detecting a corrupted slice,detecting a failed memory, or detecting a missing slice. The methodcontinues at step 430 where the processing module identifies thesub-range of the DSN address range associated with a group of memorydevices. For example, the processing module accesses the slice name tosub-range table using an identifier of the memory device.

The method continues at step 432 where the processing module facilitatesrebuilding of the identified sub-range to produce rebuilt encoded dataslices. The facilitating includes one or more of scanning for missingslices across the sub-range, acquiring a decode threshold number ofslices for each missing slice, and generating rebuilt slices from theacquired slices. The method continues at step 434 where the processingmodule updates location weights of the group of memory devices based onthe detected storage error. For example, the processing module updates alocation weight for the failed memory device to zero and raises locationweights for remaining memory devices of a group of memory devices in atotal amount equivalent to a previous location weight for the failedmemory device.

For each rebuilt encoded data slice, the method continues at step 436where the processing module identifies a corresponding memory device ofthe group of memory devices for storage of the rebuilt encoded dataslice utilizing the decentralized agreement function and the updatedlocation weights. For example, the processing module performs thedecentralized agreement function for each of the memory devices usingupdated location weights, a slice name of the rebuilt encoded dataslice, and the memory group identifier to produce ranked scoringinformation. The processing module identifies the corresponding memorydevice associated with a highest score of the ranked scoringinformation.

For each rebuilt encoded data slice, the method continues at step 438where the processing module stores the rebuilt encoded data slice in thecorresponding identified memory device. For example, the processingmodule sends the encoded data slice to the identified correspondingmemory device for each rebuilt encoded data slice.

When a Decentralized Agreement Protocol (DAP) is used internally by a DSunit for placement of slices on various memory devices of that DS unit,then every memory device within that DS unit can receive slices of anypossible name within the namespace range owned by that particular DSunit. This is in contrast to a situation in which the DS unit apportionsits namespace range into sub-ranges for each memory device within it,such that no two memory devices are responsible for the same range ofslices. The former approach of using a DAP has many advantages formigrating slices on to or off of a memory device: all memory devices areinvolved in the migration, all memory devices can maintain equalutilization, no hot-spots exist, when migrating onto a new, empty orless utilized memory device, the utilization of all other memory devicesfalls, etc. However, there is also a disadvantage when a memory devicefails, the impacted range whose health is adversely affected is greater,as is the range of names that are scanned. For example, in a DS unitwith 60 memory devices, when one memory device fails, the entire range(equivalent to that owned by 60 drives) is scanned to recover the slicesthat might have been lost by that failed memory device.

However, according to the embodiments described in association withFIGS. 41A and 41B, et al., when a slice is routed (by its name) to aparticular group of memory devices, a DAP is then used to determinewhich memory device in that group will receive the slice. This approachenables the advantages of equalized migration/balancing during faileddrives to be maintained by all memory devices within the same group thatcontains the failed drive, while serving to also limit the range that ismade unhealthy by a failure, as well as reducing the range that arescanned. As an example, consider a DS unit that contains 60 memorydevices, and apportions their namespace range into 10 parts. Each ofthese 10 sub-ranges could then be the responsibility of 6 memorydevices. Whenever a memory device is replaced, failed, or added withinthat group, the DAP is used internal to that group of memory devices toperform migration off of, or onto that memory device. However, memorydevices external to that group are not impacted and cannot be used.Should any memory device fail in a group, then only the namespace rangeowned by that group (which in this case is 1/10th of the total rangeowned by the DS unit) is impacted and has diminished health. To recoverhealth, rebuild scanning and rebuilding need only be applied across thatsub-range.

FIG. 42A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a storage unit pool 440. The DST processing unit 16 includes adecentralized agreement module 442 and the DST client module 34 ofFIG. 1. The decentralized agreement module 442 may be implementedutilizing the decentralized agreement module 350 of FIG. 40A. Thestorage unit pool 440 includes a plurality of storage units 1-N, wherethe plurality includes a subset of storage units 1-n such that n<N. Forexample, the subset of storage units includes an information dispersalalgorithm (IDA) width number n of storage units (e.g., 16) of a totalnumber of N storage units (e.g., N=20), where a decode threshold numberof the IDA is 10, and where the decode threshold number of encoded dataslices is required to recover data.

The DSN functions to access data stored as a plurality of sets ofencoded data slices in the storage unit pool, where a decentralizedagreement function is utilized to select storage units of the storageunit pool to facilitate the access. The data access includes storing thedata and retrieving the data. Hereafter, each storage unit may beinterchangeably referred to as a DST execution unit, and the storageunit pool 440/458 may be interchangeably referred to as a DSN memory.

In an example of operation of the storing of the data utilizing thedecentralized agreement function, the DST client module 34 receives adata access request 444 that includes data for storage. Having receivedthe data access request 444, the DST client module 34 determines a DSNaddress associated with the data access request. Having determined theDSN address, the DST client module 34 identifies a storage unit pool ofstorage units for storage of the data. The identifying includes at leastone of utilizing a decentralized agreement function based on the DSNaddress, performing a lookup based on the DSN address, and receiving theidentity of the storage unit pool.

Having identified the storage unit pool, the DST client module 34determines a resource level selection approach. The approach may includeat least one of storage units of the storage unit pool and storage unitsby site. The determining may be based on one or more of performing alookup, receiving the resource level selection via the data accessrequest, and interpreting a storage unit availability indicator. Forexample, the DST client module 34 selects storage units from the storageunit pool.

Having determined the resource level selection approach, the DST clientmodule 34 obtains ranked scoring information 448 for storage units ofthe storage unit pool in accordance with the resource level approach.For example, the DST client module 34 issues a rank scoring informationrequest 446 to the decentralized agreement module 442 for each storageunit of the storage unit pool using location weights of each storageunit, a storage unit pool identifier, and the DSN address. The DSTclient module 34 receives the ranked scoring information 448 inresponse.

Having received the ranked scoring information 448, the DST clientmodule 34 selects an IDA width number of storage units of the storageunit pool based on the ranked scoring information 448 and the resourcelevel selection approach. For example, the DST client module 34 selectsan IDA width number of storage units associated with a 16 highest rankedscores when the IDA width is 16 and the approach is selection by storageunit pool. As another example, the DST client module 34 selects a writethreshold number of storage units associated with highest scores. Havingselected the IDA width number of storage units, the DST client module 34issues resource access requests 450 that includes write slice requeststo the selected IDA width number of storage units. For example, the DSTclient module 34 dispersed storage error encodes the data to produce aplurality of sets of encoded data slices, generates a set of 16 writeslice requests that includes the plurality of sets of encoded dataslices, and sends, via the network 24, the set of 16 write slicerequests to the selected storage units of the storage unit pool.

The DST client module 34 receives resource access responses 452 withregards to storage of the plurality of sets of encoded data slices. Forexample, the resource access responses 452 includes one or more writeslice responses indicating status of writing encoded data slices. Whenreceiving an indication of a write failure, the DST client module 34selects another storage unit based on the ranked scoring information.For example, the DST client module 34 selects a next highest rankedstorage unit of the storage unit pool. When selecting another storageunit, the DST client module 34 sends, via the network 24, acorresponding write slice request to the selected other storage unit.The DST client module 34 issues a data access response 454 to arequesting entity based on received resource access responses 452 (e.g.,success or failure of the writing).

In an example of operation of the retrieving of the data utilizing thedecentralized agreement function, the DST client module 34 receives adata access request 444 that includes a retrieval request for the data.The DST client module 34 determines the DSN address associated with thedata access request and identifies the storage unit pool of the storageunits used for storage of the data. The DST client module 34 determinesthe resource level selection approach and obtains the rank scoringinformation 448 for the storage units of the storage unit pool inaccordance with the resource level selection approach.

Having obtained the rank scoring information 448, the DST client module34 selects a decode threshold number plus m number of storage units ofthe storage unit pool based on the ranked scoring information 448 andthe resource level selection approach (e.g., select 10+2 more storageunits with highest ranked scores when the decode threshold is 10 and theapproach is selection by storage unit pool). Having selected the storageunits, the DST client module 34 issues resource access requests 450 tothe selected storage units, where the resource access requests 450includes read slice requests. For example, the DST client module 34generates a set of read slice requests, sends, via the network 24, theread slice requests to the selected storage units, and receives resourceaccess responses 452 that includes received encoded data slices.

Having received encoded data slices, the DST client module 34 outputsanother data access response 454 to the requesting entity based on thereceived encoded data slices of the resource access responses 452. Forexample, the DST client module 34 decodes the received encoded dataslices to produce recovered data and issues the data access response 454to include the recovered data.

FIG. 42B is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distribute storage andtask (DST) processing unit 16 of FIG. 42A, the network 24 of FIG. 1, anda storage unit pool 458. The storage unit pool 458 includes a pluralityof storage units 1-20, where at least one storage unit is implemented ateach of at least two sites. For example, five storage units areimplemented at each of four sites when the number of storage units is20. Each storage unit may be implemented utilizing the storage unit 36of FIG. 1.

The DSN functions to access data stored as a plurality of sets ofencoded data slices in the storage unit pool 458, where a decentralizedagreement function is utilized to select storage units of the storageunit pool 458 to facilitate the access. The data access includes storingthe data and retrieving the data. In an example of operation, the DSTprocessing unit 16 receives a data access request 460, selects storageunits of the storage unit pool 458, issues resource access requests 466,via the network 24, to the selected storage units, receives resourceaccess responses 468, generates a data access response 470 based on thereceived resource access responses 468, and outputs the data accessresponse 470 to a requesting entity. The selecting of the storage unitsfurther includes selecting storage units based on storage unit-to-siteimplementation.

In another example of operation, when storing the data, the DSTprocessing unit 16 utilizes the decentralized agreement function toselect an information dispersal algorithm (IDA) width number of storageunits in total. Alternatively, the DST processing unit 16 selects awrite threshold number of storage units. The selecting includesdetermining a number of storage units for each site based on a number ofsites and the IDA width number. For example, the DST client module 34divides the IDA width number of 16 by 4 sites to indicate that fourstorage units per site shall be selected. Having determined the numberof storage units for selection by site, the DST client module 34utilizes the decentralized agreement function (e.g., issues a rankedscoring information request 462 to the decentralized agreement module442) to produce ranked scoring information 464 for each subset ofstorage units at each site to identify a highest ranked four of fivestorage units as the selected storage units. The DST client module 34utilizes the selected storage units for storage of the data.

When retrieving the data, the DST processing unit 16 utilizes thedecentralized agreement function to select a read threshold number ofstorage units in total (e.g., decode threshold plus two). The readthreshold number is greater than or equal to a decode threshold numberand less than or equal to the IDA width number. The selecting includesdetermining the number of storage units for each site based on thenumber of sites and the IDA width number. For example, the DST clientmodule 34 divides the IDA width number of 16 by 4 sites to indicate thatfour storage units per site shall be considered for final selection.Having determined the number of storage units for consideration by site,the DST client module 34 utilizes the decentralized agreement functionto produce ranked scoring information 464 for each subset of storageunits at each site to identify a highest ranked four of five storageunits as candidates for retrieval storage units.

Having identified candidate storage units, the DST client module 34determines a number of storage units for each site to be selected basedon the read threshold number and the number of sites. For example, theDST client module 34 divides the read threshold number of 12 by 4 sitesto indicate that three storage units per site shall be selected in thefinal selection. The DST client module 34 selects three storage unitsassociated with highest ranked scoring information of the previouslyidentified highest ranked four of five storage units per site. The DSTclient module 34 utilizes the selected storage units for the retrievalof the data.

FIG. 42C is a flowchart illustrating an example of selecting storageresources. The method begins or continues, when storing data, at step474 where a processing module (e.g., of a distributed storage and task(DST) client module) receives data for storage. The receiving mayfurther include generating a source name and updating a directory toassociate the source name with a data identifier of the data. The methodcontinues at step 476 where the processing module determines a dispersedstorage network (DSN) address based on the data access request. Forexample, the processing module performs a lookup based on the DSNaddresses.

The method continues at step 478 where the processing module identifiesa storage unit pool of storage units for storage of the data. Forexample, the processing module performs a decentralized agreementfunction based on the DSN address to select the storage unit pool from aplurality of storage unit pools. The method continues at step 480 wherethe processing module determines a resource level selection approach.The determining may be based on one or more of a predetermination, arequest, and interpreting storage unit availability.

The method continues at step 482 where the processing module obtainsranked scoring information for storage units of the storage unit pool inaccordance with the resource level selection approach. For example, theprocessing module calculates a score for each storage unit using thedecentralized agreement function based on a location weight of thestorage unit, a storage unit pool identifier, and the DSN address.

The method continues at step 484 where the processing module selects aninformation dispersal algorithm (IDA) width number of storage unitsbased on the ranked scoring information and the resource level selectionapproach. For example, for a storage unit pool approach, the processingmodule selects storage units associated with a highest score on aper-site basis, where substantially identical number of storage unitsare selected for each site associated with storage unit pool.

The method continues at step 486 where the processing module issueswrite slice requests to the selected storage units. For example, theprocessing module dispersed storage error encodes the data, generatesread slice requests, and sends the write slice requests to the selectedstorage units. Upon a write failure, the processing module issuesanother write slice request to another storage unit (e.g., associatedwith a next highest score).

The method begins or continues, when retrieving the data, at step 490where the processing module receives a retrieve request for the data.The method continues at step 492 where the processing module determinesthe DSN address based on the retrieval request. The method continues atstep 494 where the processing module identifies a storage unit pool ofstorage units for retrieval of the data. The method continues at step496 where the processing module determines the resource level selectionapproach. The method continues at step 498 where the processing moduleobtains the ranked scoring information for the storage units of thestorage unit pool in accordance with the resource level selectionapproach.

The method continues at step 500 where the processing module selects aread threshold number of storage units based on the ranked scoringinformation and the resource level selection approach. For example, theprocessing module selects a read threshold number associated withhighest scores, where a number of selected storage units per site issubstantially the same. The method continues at step 502 of theprocessing module recovers the data from the selected storage units. Forexample, the processing module generates a set of read slice requests,sends the set of read slice requests to the selected storage units,receives encoded data slices, dispersed storage error decodes thereceived encoded data slices to produce recovered data, and outputs thedata access response to a requesting entity that includes the recovereddata.

The methods described above in conjunction with the processing modulecan alternatively be performed by other modules of the dispersed storagenetwork or by other devices. In addition, at least one memory section(e.g., a non-transitory computer readable storage medium) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of the dispersed storagenetwork (DSN), cause the one or more computing devices to perform any orall of the method steps described above.

FIG. 43A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1, aDST execution (EX) unit legacy pool 510, and a DST execution unitnon-legacy pool 512. The DST processing unit 16 includes a decentralizedagreement module 514 and the DST client module 34 of FIG. 1. Thedecentralized agreement module 514 be implemented utilizing thedecentralized agreement module 350 of FIG. 40A. The DST execution unitlegacy pool 510 includes a plurality of DST execution unit generations1-G. Each DST execution unit generation includes a set of DST executionunits 1-n. Each DST execution unit may be implemented utilizing the DSTexecution unit 36 of FIG. 1. The DST execution unit non-legacy pool 512includes a plurality of DST execution unit pools 1-P. Each DST executionunit pool includes a set of DST execution units 1-n.

The DSN functions to access data stored as a plurality of sets ofencoded data slices in at least one set of DST execution units. Theaccessing of the data includes selecting the set of DST execution unitsusing one or more of a decentralized agreement function and a DSNaddressing mapping. As a specific example, the DST processing unit 16receives a data access request 516; selects one of the DST executionunit legacy pool 510 and a particular set of DST execution units of DSTexecution unit non-legacy pool 512 using the decentralized agreementfunction, where the DST processing unit utilizes the DSN addressingmapping to further select a DST execution unit generation when selectingthe DST execution unit legacy pool; accesses, issuing resource accessrequests 522 and receiving resource access responses 524, at least oneof the particular set of DST execution units of the DST execution unitnon-legacy pool and the further selected DST execution unit generation;and issues a data access response 526 based on the received resourceaccess responses 524.

In an example of operation of the accessing of the data, the DST clientmodule 34 obtains a DSN address associated with the data access request516 and obtains ranked scoring information 520 or the DST execution unitlegacy pool and one or more DST execution unit pools of the DSTexecution unit non-legacy pool. For example, the DST client module 34issues a ranked scoring information request 518 to the decentralizedagreement module 514 utilizing location weights associated with each DSTexecution unit legacy pool and the DST execution unit non-legacy pool, astorage pool identifier, and the DSN address; and receives the rankedscoring information 520. For instance, a location weight of 800 isassociated with the DST execution unit legacy pool, a location weight of100 is associated with DST execution unit pool 1, etc., and a locationweight of 300 is associated with DST execution unit pool P.

Having obtained the ranked scoring information 520, the DST clientmodule 34 selects one of the DST execution unit legacy pool and one ofthe one or more DST execution unit pools of the DST execution unitnon-legacy pool based on the ranked scoring information 520. Forexample, the DST client module 34 performs the selection by identifyinga pool associated with a highest score.

When selecting the DST execution unit legacy pool, the DST client module34 accesses, in accordance with the data access request, a DST executionunit generation that corresponds to the DSN address (e.g., identify theDST execution unit generation based on a generation field of the DSNaddress). The accessing includes issuing resource access requests 522(e.g., write slice requests, read slice request, delete slice requests,list slice requests, etc.). The accessing further includes receivingresource access responses 524 (e.g., write slice responses, read sliceresponses, delete slice responses, list slice responses, etc.).

When selecting the one of the one or more DST execution unit pools, theDST client module 34 accesses, in accordance with the data accessrequests, the selected DST execution unit pool. The accessing includesissuing the resource access requests 522 and receiving the resourceaccess responses 524. Having received the resource access responses 524,the DST client module 34 issues the data access response 526 to arequesting entity based on the received resource access responses 524.

FIG. 43B is a flowchart illustrating another example of selectingstorage resources slices. The method begins or continues at step 530where a processing module (e.g., of a distributed storage and task (DST)client module) obtains a dispersed storage network (DSN) addressassociated with a data access request from a requesting entity. Theobtaining includes at least one of generating, performing a lookup, andreceiving. The method continues at step 532 where the processing moduleobtains ranked scoring information for a legacy storage unit pool andone or more non-legacy storage unit pools based on the DSN address. As aspecific example, the processing module utilizes a decentralizedagreement function to generate a score of the ranked scoring informationfor each storage pool using the location weights of the storage pool, astorage pool identifier, and the DSN address.

The method continues at step 534 where the processing module selects oneof the legacy storage unit pool and the one or more non-legacy storagepools based on the ranked scoring information to produce a selectedstorage unit pool. For example, the processing module identifies a poolassociated with a highest score and selects the pool associated with thehighest score as the selected storage unit pool.

When the selected storage unit pool includes the legacy storage unitpool, the method continues at step 536 where the processing moduleaccesses resources of the legacy storage unit pool in accordance withthe data access request and based on the DSN address. For example, theprocessing module selects a storage unit generation based on ageneration field of the DSN address, issues write slice requests to theselected storage unit generation for a store data access request orissues read slice requests to the selected storage unit generation for aretrieve data access request, and receives responses.

When the selected storage unit pool includes one non-legacy storage unitpool, the method continues at step 538 where the processing moduleaccesses resources of the non-legacy storage unit pool in accordancewith the data access request. For example, the processing module issueswrite slice requests to storage units of the selected storage unit poolfor the store data request or issues read slice requests to the storageunits of the selected storage unit pool for the retrieve data accessrequest, and receives the responses. When receiving access responses,the processing module generates a data access response based on thereceived access responses and outputs the data access response to therequesting entity.

FIG. 44A is a schematic block diagram of another embodiment of adistributed storage and task (DST) execution (EX) unit 540 that includesthe processing module 84 of FIG. 3 and the memory device 88 of FIG. 3.The DST execution unit 540 functions to provide access to slices 542stored in the memory device 88. The accessing includes storing andretrieving.

An example of operation of the storing, the processing module 84receives a write slice requests that includes an encoded data slice forstorage and a slice name of the encoded data slice. For example, theprocessing module 84 receives an encoded data slice with a slice name ofA-1. The processing module 84 obtains a bucket file for storage of theencoded data slice. The processing module 84 organizes a portion of thememory device 88 to provide a plurality of bucket files 1-B, where eachbucket file may be utilized to store one or more encoded data slices.Each bucket file may be fixed or variable in size. Each bucket file maybe unique in size or substantially the same. The obtaining of the bucketfile includes selecting an existing bucket file, where the existingbucket file includes available space and generating a new bucket filewhen a size of the received encoded data slices greater than availablespace of the existing bucket file. For example, the processing module 84selects bucket file 1 for storage of the encoded data slice A-1.

Having obtained the bucket file, the processing module 84 selects anoffset within the selected bucket file, where sufficient space existswithin the bucket file starting at the offset for the encoded dataslice, a start delimiter, and an end a delimiter. Alternatively, theoffset may further include a memory device identifier when a pluralityof memory devices 88 are utilized. The selecting may include accessingan offset list 546 from the memory device 88, where the offset listincludes associations of slice names, bucket file identifiers, andoffsets. For example, the processing module 84 selects an offset of 300based on available storage space for encoded data slice A-1.

Having selected the offset, the processing module 84 updates the offsetlist 546 to associate the slice name, the selected bucket file, and theselected offset. For example, the processing module 84 associates slicename A-1 with bucket file 1 at an offset of 300.

Having updated the offset list 546, the processing module 84 generatesthe start and end delimiters for the encoded data slice. As a specificexample, the processing module 84 performs a deterministic function on acombination of a random parameter hundred and 44, the bucket fileidentifier, and a start or and slice indicator. The deterministicfunction includes at least one of a hashing function, a hash-basedmessage authentication code function, a mask generating function, and asponge function. The random parameter 544 may be associated with atleast one of DST execution unit and each bucket file. As a specificexample, the processing module 84 generates the random parameter 544when a new bucket file is created using at least one of a cryptographicsecure random number generator, a pseudo random number generator, andentropy source generator, a key generator, and a random seed. Theprocessing module 84 stores the random parameter 544 in the memorydevice 88 and may further store an association indicator indicatingwhether the rent parameters associated with the DST execution unit or aparticular bucket file.

Having generated the start and end delimiters, the processing module 84issues a write slice rejection response to a requesting entity whendetecting either of the start and end delimiters within the encoded dataslice. When not detecting either of the start and end delimiters withinthe encoded data slice, the processing module 84 stores, starting at theoffset within the selected bucket list, the start delimiter, the encodeddata slice, and the end delimiter.

An example of operation of the retrieving, the processing module 84receives a read slice requests that includes the slice name. Theprocessing module 84 identifies the bucket file and offset for retrievalof the encoded data slice by accessing the offset list based on a slicename. Having identified the bucket file an offset, the processing module84 accesses the bucket file within the memory 88 using the offset toidentify the start delimiter associated with the encoded data slice.Having identified the start delimiter, the processing module 84 extractsencoded data slice from the bucket file immediately after the startdelimiter and ending when identifying the end delimiter. Havingextracted the encoded data slice, the processing module 84 sends theencoded data slice to a requesting entity.

FIG. 44B is a flowchart illustrating an example of de-marking encodeddata slices. The method begins or continues, when storing an encodeddata slice, at step 550 where a processing module (e.g., of adistributed storage and task (DST) client module) receives a write slicerequest that includes the encoded data slice and a slice name. Themethod continues at step 552 where the processing module obtains abucket file for storage of the encoded data slice. For example, theprocessing module selects an existing bucket file associated withsufficient storage space. As another example, the processing modulegenerates a new bucket file when not locating an existing bucket filewith sufficient space.

The method continues at step 554 where the processing module selects anoffset within the bucket file for storage of the encoded data slice. Forexample, the processing module identifies a space between two existingoffsets associated with sufficient space for storage of the encoded dataslice and identifies the offset of the start of the identified space.Alternatively, the offset may further include identification of a memorydevice of the plurality of memory devices.

The method continues at step 556 where the processing module updates anoffset list to associate the slice name, the selected bucket file, andthe selected offset. For example, the processing module recovers theoffset list, updates the offset list to produce an updated offset list,and stores the updated offset list.

The method continues at step 558 where the processing module generatesstart and end delimiters for the encoded data slice based on the bucketfile. For example, the processing module performs a deterministicfunction on a combination of one or more of a random parameter, a bucketfile identifier, and a start or and slice indicator. The methodcontinues at step 560 where the processing module issues a write slicerejection response to a requesting entity when detecting either of thestart and end delimiters within the encoded data slice. The methodcontinues at step 562 where the processing module stores, starting atthe offset within the selected bucket file, the start delimiter, theencoded data slice, and the end delimiter when not detecting either ofthe start and end delimiters within the encoded data slice.

FIGS. 45A-45E are a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the distributed storage andtask network (DSTN) managing unit 18 of FIG. 1, the network 24 of FIG.1, and storage sets 1 and 2. The DST processing unit 16 includes adecentralized agreement module 570 and the DST client module 34 ofFIG. 1. The decentralized agreement module 570 may be implementedutilizing the decentralized agreement module 350 of FIG. 40A. The DSTNmanaging unit 18 includes the decentralized agreement module 570 and theDST client module 34 of FIG. 1. Each storage set includes a set of n DSTexecution (EX) units. Each DST execution unit includes the decentralizedagreement module 570, the DST client module 34 of FIG. 1, and the memory88 of FIG. 3. Each DST execution unit may be implemented utilizing theDST execution unit 36 of FIG. 1. Hereafter, each DST execution unit maybe interchangeably referred to as a storage unit, a storage set may beinterchangeably referred to as a set of storage units, and the storagesets 1 and 2 may be interchangeably referred to as a DSN memory. The DSNfunctions to access data while migrating storage of the data, where theDST processing unit 16 dispersed storage error encodes the data toproduce a plurality of sets of encoded data slices 574 and stores theplurality of sets of encoded data slices 574 in at least one storage setto store the data.

FIG. 45A illustrates steps of an example of operation of the accessingof the data while migrating the storage of the data where the DSTNmanaging unit 18 issues, via the network 24, pending weights 572 to thestorage units 1-n of the storage sets 1 and 2. The pending weights 572include future weighting factors for one or more storage resources ofthe DSN memory. Examples of future weighting factors includes a futureweighting factor for a memory to be decommissioned, a future weightingfactor for a memory to be commissioned, a future weighting factor for astorage unit to be decommissioned, the future weighting factor for astorage unit to be commissioned, a future weighting factor for a storageset to be decommissioned, and a future weighting factor for a storageset to be commissioned.

The issuing of the pending weights 572 includes one or more of detectingweighting factor changes, generating the pending weights 572 based onthe detected weighting factor changes, identifying effected storageunits of the storage sets, and sending, via the network 24, the pendingweights 572 to the effected storage units of the storage sets. Theidentifying of the effected storage units includes identifying at leastone DSN address range associated with the effected storage units, whereperforming a distributed agreement protocol function on the DSN addressrange utilizing either of the pending weights 572 and current weights(e.g., before upcoming changes when the pending weights are madecurrent) produces ranked scoring information that identifieshighest-ranking storage resources to produce the effected storage units.For instance, the DST client module 34 of the DSTN managing unit 18utilizes the decentralized agreement module 570 to identify storageunits of the storage set 1 and storage units of the storage set 2 aseffected by the pending weights 572.

The plurality of storage units of the DSN receives the pending weights572 as updated properties of the DSN memory, where the updatedproperties of the DSN memory requires storage migration within the DSNmemory. For example, the storage migration is required to select andmigrate slices 574 as transfer slices 576 from storage units of thestorage set 1 to the storage units of the storage set 2 when the futureweighting factors of the storage units of the storage set 1 are loweredand the future weighting factors of the storage units of the storage set2 are raised.

Having received the updated properties of the DSN memory (e.g.,including pending weights 572), a first storage unit and a secondstorage unit of the plurality of storage units establish a migrationpairing based on the updated properties of the DSN memory.Alternatively, the DST client module 34 of the DSTN managing unit 18establishes the migration pairing. The establishing of the migrationpairing may include performing, by the first storage unit, a scoringfunction (e.g., the distributed agreement protocol function by thedecentralized agreement module 570) using one or more properties of DSNaccess information (e.g., a DSN address range associated with a givenstorage set) and one or more properties of non-updated properties of theDSN memory (e.g., current weighting factors of DSN memory resources) toidentify a range of DSN addresses affiliated with the first storageunit, performing, by the second storage unit, the scoring function usingthe one or more properties of DSN access information and the one or moreproperties of non-updated properties of the DSN memory to identify therange of DSN addresses affiliated with the first storage unit,performing, by the first storage unit, an updated scoring function usingthe one or more properties of DSN access information and one or moreproperties of the updated properties of the DSN memory (e.g., thepending weights 572) to identify a range of DSN addresses affiliatedwith the second storage unit (e.g., where changes), performing, by thesecond storage unit, the updated scoring function using the one or moreproperties of DSN access information and the one or more properties ofthe updated properties of the DSN memory to identify the range of DSNaddresses affiliated with the second storage unit, and establishing, bythe first and second storage units, the migration pairing based on therange of DSN addresses being affiliated with the first storage unitbased on the non-updated properties of the DSN memory and the range ofDSN addresses being affiliated with the second storage unit based on theupdated properties of the DSN memory.

As a specific example of the establishing of the migration pairing, theDST execution unit 1-2 and the DST execution unit 2-2 identify a DSNaddress range affiliated with the DST execution unit 1-2 (e.g.,associated with slices 574 currently stored within the memory 88 of theDST execution unit 1-2) effected by the pending weights 572, whereencoded data slices associated with the identified DSN address range areto be migrated as the transfer slices 576 from the DST execution unit1-2 to the DST execution unit 2-2. With the migration pairingestablished, the first and second storage units establish, between thefirst and second storage units, a storage migration mechanism formigrating storage of data between the first and second storage unitsbased on the updated properties of the DSN memory. Alternatively, theDSTN managing unit 18 establishes the storage migration mechanism.

The establishing of the storage migration mechanism may include one ormore of identifying an address range to migrate, identifying stored datahaving an address within the address range to migrate, establishing adata migration list that includes the identified stored data,establishing a data migration pattern for migrating the identifiedstored data between the first and second storage units, and updating thedata migration list as the identified stored data is migrated betweenthe first and second storage units. For example, the DST execution unit1-2 identifies the DSN address range associated with stored encoded dataslices 574, where the stored encoded data slices 574 are associated withthe DST execution unit 1-2 when utilizing the non-updated properties ofthe DSN memory (e.g., current weighting factors), establishes the datamigration list to include slice names of the stored encoded data slices574 associated with the DSN address range, establishes the datamigration pattern that includes sending, via the network 24, theidentified stored encoded data slices 574 as transfer slices 576 to theDST execution unit 2-2, and facilitates the migration of the transferslices 576, and updates the data migration list as the transfer slices576 are confirmed to be received and stored in the memory 88 of the DSTexecution unit 2-2.

The establishing of the storage migration mechanism may further includedetermining, based on the non-updated properties of the DSN memory(e.g., current weighting factors), a source storage unit of the firstand second storage units, determining, based on the updated propertiesof the DSN memory, a destination storage unit of the first and secondstorage units, and sending the identified stored data from the sourcestorage unit to the destination storage unit. For example, the pairingof the DST execution units 1-2 and 2-2 determines that the DST executionunit 1-2 is the source storage unit, determines that the DST executionunit 2-2 is the destination storage unit, and DST execution unit 1-2sends the identified encoded data slices of the identified stored dataas transfer slices 576 to the DST execution unit 2-2 for storage.

FIG. 45B illustrates further steps of the example of operation of theaccessing of the data while migrating the storage of the data, whereencoded data slices associated with the identified DSN address range aremigrated from the DST execution unit 1-2 to the DST execution unit 2-2.For example, the DST execution unit 1-2 identifies encoded data slicesA-1 through A-N of a transfer range 1 of the DSN address range andexcludes encoded data slices B-1 through B-N from the store datamigration (e.g., not associated with the identified DSN address range),where the memory 88 of the DST execution unit 1-2 stores the storedencoded data slices 574.

While migrating the storage of the data between the first and secondstorage units in accordance with the storage migration mechanism, thefirst or second storage unit receives a data access request (e.g., newdata object write request, new revision data object write request, readrequest) regarding the data effected by the migrating the storage ofdata between the first and second storage units. The receiving the dataaccess request may include receiving the data access request by thefirst storage unit when the data access request was created inaccordance with the updated properties of DSN memory and receiving thedata access request by the second storage unit when the data accessrequest was created in accordance with non-updated properties of DSNmemory.

For example, with the migrating of the storage of the data initiated,where at a given point in time a portion of the encoded data slicesassociated with the identified DSN address range have been successfullymigrated and a remaining portion of the encoded data slices associatedwith the identified DSN address range have not yet been successfullymigrated (e.g., encoded data slices A-1 and A-2 have been transferredfrom the DST execution unit 1-2 to the DST execution unit 2-2), arequesting entity (e.g., the DST processing unit 16) attempts to accessthe DSN memory to access one or more of the encoded data slicesassociated with the identified DSN address range. As a specific example,the DST client module 34 of the DST processing unit 16 utilizes thedecentralized agreement module 570 to perform the distributed agreementprotocol function on a DSN address associated with a desired data objectof access using the non-updated DSN properties (e.g., current weightingfactors of the storage units) to identify the DST execution unit 1-2 asaffiliated with the encoded data slices A-1 through A-N and sends, viathe network 24, slice requests to the DST execution unit 1-2 withregards to accessing at least some of the encoded data slices A-1through N-N. For instance, the DST processing unit 16 sends slicerequests A-1 and A-2 to the DST execution unit 1-2 based on non-updatedDSN properties (e.g., current weighting factors). The example ofoperation is continued as discussed with reference to FIG. 45C.

FIG. 45C illustrates further steps of the example of operation of theaccessing of the data while migrating the storage of the data where thefirst storage unit or the second storage unit determines status of themigrating storage of data between the first and second storage units.For example, the DST execution units 1-2 and 2-2 determine that thestatus of the migrating storage of the data indicates that the encodeddata slices A-1 and A-2 have been successfully transferred and encodeddata slices A-3 through A-N are pending transfer. The determining of thestatus may be facilitated in accordance with a status determiningapproach based on a type of the data access request (e.g., read request,new write request, revision write request). When the type of the dataaccess request is the read request, the determining the status of themigrating storage of the data includes accessing the migration list ofdata being migrated between the first and second storage units,determining whether a data object of the read request has been migratedbased on the migration list, when the data object has been migrated,indicating the status as migrated to destination, and when the dataobject has not been migrated, indicating the status as not migrated todestination.

When the type of the data access request is the new write request, thedetermining the status of the migrating storage of the data includes,when the first and second storage units possess the updated propertiesof the DSN memory, setting the status for the new write request as writeto destination (e.g., indicate that the DST execution unit 2-2 is toexecute the new write request). When the type of the data access requestis the revision write request for a revised data object, the determiningthe status of the migrating storage of the data includes accessing themigration list of data being migrated between the first and secondstorage units, determining whether a predetermined number (e.g., all oralmost all) of data objects on the migration list have been migrated toa destination, when the predetermined number of data objects have beenmigrated, indicating the status as migrated to destination, and when thepredetermined number of data objects have not been migrated, indicatingthe status as not migrated to destination.

Having determined the status, the first storage unit or the secondstorage unit determines which of the first and second storage units isto process the data access request based on the status to produce adetermined storage unit. The determining may be based on the type of thedata access request. When the type of the data access request is theread request, the determining the determined storage unit includesdetermining that the first storage unit is the determined storage unitwhen the read request was created based on non-updated properties of theDSN memory and the status is not migrated to destination, determiningthat the second storage unit is the determined storage unit when theread request was created based on the non-updated properties of the DSNmemory and the status is migrated to destination (e.g., the DSTexecution unit 2-2 is identified to process the data access request whenthe data access request as the read request and the encoded data slicesA-1 and A-2 has been successfully migrated to the DST execution unit2-2), determining that the first storage unit is the determined storageunit when the read request was created based on updated properties ofthe DSN memory and the status is not migrated to destination, anddetermining that the second storage unit is the determined storage unitwhen the read request was created based on the updated properties of theDSN memory and the status is migrated to destination.

When the type of the data access request is the new write request, thedetermining the determined storage unit includes when the statusindicates write to destination, performing an updated scoring functionusing one or more properties of the new write request and one or moreproperties of the updated properties of the DSN memory to identify thesecond storage unit as the determined storage unit. When the type of thedata access request is the revision write request for the revised dataobject, the determining the determined storage unit includes when thestatus indicates migrated to destination, identifying the second storageunit as the destination and as the determined storage unit, and when thestatus indicates not migrated to destination, identifying the firststorage unit as a source and as the determined storage unit.

Having determined the determined storage unit, the determined storageunit processes the data access request. The processing may be based onthe type of the data access request. When the type of the data accessrequest is the read request, the processing the data access requestincludes the determined storage unit processes the read request. Theprocessing may include forwarding, by another storage unit, the readrequest to the determined storage unit. For example, the DST executionunit 1-2 forwards, via the network 24, the slice request A-1, A-2 as aforward slice request A-1, A-2 to the DST execution unit 2-2 when theDST execution unit 2-2 is the determined storage unit for the readrequest and the DST execution unit 2-2 issues, via the network 24, aslice response A-1, A-2 that includes the encoded data slices A-1 andA-2 to the DST processing unit 16.

When the type of the data access request is the new write request, theprocessing the data access includes storing the data object by thesecond storage unit and updating the migration list to include that thedata object has been migrated to the destination. The processing mayinclude forwarding the new write request to the determined storage unit.For example, the DST execution unit 1-2 forwards the write request(e.g., write slice request) to the DST execution unit 2-2, the DSTexecution unit 2-2 stores new encoded data slices of the new data objectin the memory 88, and issues a write slice response A-1, A-2 to the DSTprocessing unit 16 indicating status of the new write request.

When the type of the data access request is the revision write requestfor the revised data object, the processing the data access includes,when the status indicates migrated to destination, storing the reviseddata object by the second storage unit, and updating the migration listto include that the revised data object has been migrated to thedestination, and, when the status indicates not migrated to thedestination, storing the revised data object by the first storage unit,and updating the migration list to include that the revised data objecthas not been migrated to the destination. For example, when the statusindicates migrated to destination, the DST execution unit 1-2 forwardsthe revision write request to the DST execution unit 2-2. As anotherexample, when the status indicates that migrated to the destination, theDST execution unit 1-2 stores the revision encoded data slices in thememory 88 of the DST execution unit 1-2 and updates the migration listto include that the device data object has not been migrated to thedestination (e.g., default).

FIG. 45D illustrates further steps of the example of operation of theaccessing of the data while migrating the storage of the data where therequesting entity of the data access utilizes the updated DSN properties(e.g., the pending weights) to identify the DST execution unit for thedata access request and sends a corresponding slice access request tothe identified DST execution unit. For example, the DST processing unit16 issues, via the network 24, a slice request A-3, A-4 to the DSTexecution unit 2-2 when the corresponding encoded data slices A-3, A-4have not yet been migrated from the DST execution unit 1-2 to the DSTexecution unit 2-2.

Having received the data access request, the first storage unit or thesecond storage unit determines the status of the migrating the storageof the data between the first and second storage units. For instance,the first and second storage units determined that the status indicatesthat only encoded data slices A-1 and A-2 have been successfullytransferred to the DST execution unit 2-2. Having determined the status,the first or second storage units determine which of the first andsecond storage units is to process the data access request based on thestatus to produce the determined storage unit. For example, the DSTexecution units 1-2 and 2-2 determine that the DST execution unit 1-2shall process the data access request as the determined storage unitwhen the encoded data slices A-3 and A-4 of the data access request havenot yet been transferred.

Having identified the determined storage unit, the determined storageunit processes the data access request. For example, the DST executionunit 2-2 forwards, via the network 24, the slice request A-3, A-4 to theDST execution unit 1-2, the DST execution unit 1-2 processes the dataaccess request to produce a slice response A-3, A-4, and sends, via thenetwork 24, the slice response A-3, A-4 to the DST processing unit 16.

FIG. 45E illustrates further steps of the example of operation of theaccessing of the data while migrating the storage of the data where thedetermined storage unit (e.g., DST execution unit 2-2) issues, via thenetwork 24, a transfer complete message (e.g., transfer complete A-1through A-N) to the DSTN managing unit 18 indicating that the identifiedencoded data slices of the identified DSN address range for migrationhave been successfully transferred from the DST execution unit 1-2 tothe DST execution unit 2-2. Having received the transfer completemessage, the DSTN managing unit 18 issues, via the network 24, confirmedweights 578 to one or more entities of the DSN. The confirmed weights578 include the updated DSN parameters (e.g., updated weighting factorsassociated with the affected storage units of the migration of thestored data). For example, the DSTN managing unit 18, sends, theconfirmed weights 578 to the DST processing unit 16 for utilization inaccessing the DSN memory.

FIG. 45F is a flowchart illustrating an example of accessing data whilemigrating storage of the data. In particular, a method is presented foruse in conjunction with one or more functions and features described inconjunction with FIGS. 1-39, 45A-E, and also FIG. 45F. The method beginsor continues at step 580 where a plurality of storage units, thatincludes one or more processing module of one or more computing devicesof one or more computing devices of a dispersed storage network (DSN),receives updated properties of DSN memory, where the DSN memory includesthe plurality of storage units and where the updated properties of theDSN memory requires storage migration within the DSN memory.

The method continues at step 582 where a first storage unit and a secondstorage unit of the plurality of storage units establish a migrationpairing based on the updated properties of the DSN memory. Theestablishing the migration pairing may include performing, by the firststorage unit, a scoring function using one or more properties of DSNaccess information and one or more properties of non-updated propertiesof the DSN memory to identify a range of DSN addresses affiliated withthe first storage unit, performing, by the second storage unit, thescoring function using the one or more properties of DSN accessinformation and the one or more properties of non-updated properties ofthe DSN memory to identify the range of DSN addresses affiliated withthe first storage unit, performing, by the first storage unit, anupdated scoring function using the one or more properties of DSN accessinformation and one or more properties of the updated properties of theDSN memory to identify a range of DSN addresses affiliated with thesecond storage unit, performing, by the second storage unit, the updatedscoring function using the one or more properties of DSN accessinformation and the one or more properties of the updated properties ofthe DSN memory to identify the range of DSN addresses affiliated withthe second storage unit, and establishing, by the first and secondstorage units, the migration pairing based on the range of DSN addressesbeing affiliated with the first storage unit based on the non-updatedproperties of the DSN memory and the range of DSN addresses beingaffiliated with the second storage unit based on the updated propertiesof the DSN memory.

The method continues at step 584 where the first and second storageunits establish, between the first and second storage units a storagemigration mechanism for migrating storage of data between the first andsecond storage units based on the updated properties of the DSN memory.The establishing of the storage migration mechanism may includeidentifying an address range to migrate, identifying stored data havingan address within the address range to migrate, establishing a datamigration list that includes the identified stored data, establishing adata migration pattern for migrating the identified stored data betweenthe first and second storage units, and updating the data migration listas the identified stored data is migrated between the first and secondstorage units. The establishing of the storage migration mechanism mayfurther include the first or second storage units determining, based onnon-updated properties of the DSN memory, a source storage unit of thefirst and second storage units, determining, based on the updatedproperties of the DSN memory, a destination storage unit of the firstand second storage units, and sending the identified stored data fromthe source storage unit to the destination storage unit.

While migrating the storage of data between the first and second storageunits in accordance with the storage migration mechanism, the methodcontinues at step 586 where the first storage unit or the second storageunit receives a data access request (e.g., new data object writerequest, new revision data object write request, read request) regardingdata effected by the migrating the storage of data between the first andsecond storage units. The receiving the data access request may includereceiving the data access request by the first storage unit when thedata access request was created in accordance with the updatedproperties of DSN memory and receiving the data access request by thesecond storage unit when the data access request was created inaccordance with non-updated properties of DSN memory.

The method continues at step 588 where the first or second storage unitdetermines status of the migrating storage of data between the first andsecond storage units. The determining of the status may be facilitatedin accordance with a status determining approach based on a type of thedata access request (e.g., read request, new write request, revisionwrite request). When the type of the data access request is the readrequest, the determining the status of the migrating storage of the dataincludes accessing the migration list of data being migrated between thefirst and second storage units, determining whether a data object of theread request has been migrated based on the migration list, when thedata object has been migrated, indicating the status as migrated todestination, and when the data object has not been migrated, indicatingthe status as not migrated to destination.

When the type of the data access request is the new write request, thedetermining the status of the migrating storage of the data includes,when the first and second storage units possess the updated propertiesof the DSN memory, setting the status for the new write request as writeto destination. When the type of the data access request is the revisionwrite request for a revised data object, the determining the status ofthe migrating storage of the data includes accessing the migration listof data being migrated between the first and second storage units,determining whether a predetermined number (e.g., all or almost all) ofdata objects on the migration list have been migrated to a destination,when the predetermined number of data objects have been migrated,indicating the status as migrated to destination, and when thepredetermined number of data objects have not been migrated, indicatingthe status as not migrated to destination.

The method continues at step 590 where the first storage unit or thesecond storage unit determines which of the first and second storageunits is to process the data access request based on the status toproduce a determined storage unit. The determining may be based on thetype of the data access request. When the type of the data accessrequest is the read request, the determining the determined storage unitincludes determining that the first storage unit is the determinedstorage unit when the read request was created based on non-updatedproperties of the DSN memory and the status is not migrated todestination, determining that the second storage unit is the determinedstorage unit when the read request was created based on the non-updatedproperties of the DSN memory and the status is migrated to destination,determining that the first storage unit is the determined storage unitwhen the read request was created based on updated properties of the DSNmemory and the status is not migrated to destination, and determiningthat the second storage unit is the determined storage unit when theread request was created based on the updated properties of the DSNmemory and the status is migrated to destination.

When the type of the data access request is the new write request, thedetermining the determined storage unit includes when the statusindicates write to destination, performing an updated scoring functionusing one or more properties of the new write request and one or moreproperties of the updated properties of the DSN memory to identify thesecond storage unit as the determined storage unit. When the type of thedata access request is the revision write request for the revised dataobject, the determining the determined storage unit includes when thestatus indicates migrated to destination, identifying the second storageunit as the destination and as the determined storage unit, and when thestatus indicates not migrated to destination, identifying the firststorage unit as a source and as the determined storage unit.

The method continues at step 592 where the determined storage unitprocesses the data access request. The processing may be based on thetype of the data access request. When the type of the data accessrequest is the read request, the processing the data access requestincludes the determined storage unit processes the read request. Theprocessing may include forwarding, by another storage unit, the readrequest to the determined storage unit. When the type of the data accessrequest is the new write request, the processing the data accessincludes storing the data object by the second storage unit and updatingthe migration list to include that the data object has been migrated tothe destination. The processing may include forwarding the new writerequest to the determined storage unit. When the type of the data accessrequest is the revision write request for the revised data object, theprocessing the data access includes, when the status indicates migratedto destination, storing the revised data object by the second storageunit, and updating the migration list to include that the revised dataobject has been migrated to the destination, and, when the statusindicates not migrated to the destination, storing the revised dataobject by the first storage unit, and updating the migration list toinclude that the revised data object has not been migrated to thedestination.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other devices. In addition, at least one memory section(e.g., a non-transitory computer readable storage medium) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices of the dispersed storagenetwork (DSN), cause the one or more computing devices to perform any orall of the method steps described above.

FIG. 46A is a schematic block diagram of another embodiment of adistributed storage and task network (DSTN) that includes thedistributed storage and task (DST) processing unit 16 of FIG. 1, thenetwork 24 of FIG. 1, and the DSTN module 22 of FIG. 1. The DSTprocessing unit 16 includes the DST client module 34 of FIG. 1 and adecentralized agreement module 600. The decentralized agreement module600 may be implemented utilizing the decentralized agreement module 350of FIG. 40A. The DSTN module 22 includes at least one set of DSTexecution (EX) units 1-n. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1. Each DST execution unitincludes the distributed task (DT) execution module 90 of FIG. 3.

The DSTN functions to execute a task 94 to generate a result 104. Forexample, the DST client module 34 generates partial tasks 98 from areceived task 94, selects one or more DST execution units for executionof the partial tasks 98 using a decentralized agreement function, sends,via the network 24, the partial tasks 98 to the one or more selected DSTexecution units, receives partial results 102, generates a result 104based on the partial results 102, and outputs the results 104 to arequesting entity. The selecting utilizing the decentralized agreementfunction includes utilizing location weights associated with each DTexecution module 90, where the location weight is associated with apartial task execution capability level. For example, the locationweight of 800 is associated with the DT execution module 90 of DSTexecution unit 1, a location weight of 400 associated with the DTexecution module 90 of DST execution unit 2, etc.

In a further example of operation, the DST client module 34 receives thetask 94 for execution. The DST client module 34 obtains the locationweights for each DT execution module 90 of the set of DST executionunits 1-n. The obtaining includes at least one of performing a lookup,receiving, and issuing a query to at least some of the DST executionunits. Having obtained the location weights, the DST client module 34obtains ranked scoring information 604 for the plurality of DT executionmodules 90 based on the location weights. For example, the DST clientmodule 34 issues a ranked scoring information request 602 to thedecentralized agreement module 600, where the request 602 includes thelocation weights of each DT execution module 90, a DT execution moduleidentifier, a DT execution module group identifier, and a taskidentifier of the task 94; and receives the ranked scoring information.

Having obtained the ranked scoring information 604, the DST clientmodule 34 determines a number of DT execution modules 90 for assignmentto the task 94. The determining may be based on one or more of acapability level of the DT execution modules 90 and scores associatedwith each DT execution module of the ranked scoring information 604. Forexample, the DST client module 34 selects five DT execution modules 90associated with a highest five scores of the ranked scoring information604, where the five DT execution modules 90 are associated withsufficient task execution capability levels to execute five partialtasks 98 in accordance with a required time frame.

Having determined the number of DT execution modules 90, the DST clientmodule 34 generates the number of partial tasks 98 based on the task 94.For example, the DST client module 34 generates one partial task 98 foreach DT execution module 90 of the selected five DT execution modules90. As another example, the DST client module 34 generates two partialtasks for a DT execution module 90 associated with a highest score andgenerates one partial task for each remaining DT execution module 90 ofthe selected five DT execution modules 90.

The DST client module 34 issues the generated partial tasks 98, via thenetwork 24, to the selected DT execution modules 90. The DST clientmodule 34 receives partial results 102 and issues the result 104 basedon the received partial results 102.

FIG. 46B is a flowchart illustrating an example of selecting taskexecution resources. The method begins or continues at step 606 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule) receives a task for execution, where the task is associated witha task identifier (ID). The method continues at step 608 where theprocessing module obtains location weights for each of the plurality oftask execution units. The obtaining includes at least one of receivingthe location weights with the task, performing a lookup, initiating aquery, and receiving a query response.

The method continues at step 610 where the processing module determinesranked scoring information for the plurality of task execution unitsbased on the location weights. For example, the processing moduleperforms a decentralized agreement protocol function using the locationweights of each task execution unit, a task execution unit identifier, atask execution unit group identifier, and the task ID to produce theranked scoring information.

The method continues at step 612 where the processing module determinesa number of resources to assign to execution of the task based on thetask. The determining may be based on one or more of a desired taskexecution completion time frame, resource availability, and a number oftask execution units associated with a score above a score thresholdlevel. The method continues at step 614 where the processing modulegenerates at least one partial task for each of the number of resourcesexecuting the task. For example, the processing module divides up thetask into partial tasks. As another example, the processing modulereplicates partial tasks. As yet another example, the processing modulereplicates the task as the partial tasks.

The method continues at step 616 where the processing module selects thenumber of resources of the plurality of task execution units based onthe ranked scoring information to produce one or more selected taskexecution units. For example, the processing module selects taskexecution units associated with highest scores of the ranked scoringinformation in accordance with the determined number of resourcesexecuting the task.

The method continues at step 618 where the processing module sends acorresponding partial task to each of the one or more selected taskexecution units. The method continues at step 620 where the processingmodule issues a result based on received partial results.

FIG. 47A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a DST execution (EX) unit pool 622. The DST processing unit 16includes the DST client module 34 of FIG. 1. The DST execution unit pool622 includes at least one set of DST execution units 1-n. Each DSTexecution unit may be implemented utilizing the DST execution unit 36 ofFIG. 1.

The DSN functions to update storage unit configuration of the DSTexecution unit pool 622, where the DST execution unit pool 622 storespluralities of sets of encoded data slices. The updating of the storageunit configuration includes one or more of activating a storage unit,deactivating a storage unit, upgrading storage unit software, upgradingstorage unit hardware, and performing a maintenance test.

In an example of operation, each DST execution unit determines a slicestorage status for a DSN address range based on monitoring rebuildingmessages. The slice storage status includes one or more of the DSNaddress range, a number of storage errors, a status rating, an overallslice storage status, and a number of favorably stored encoded dataslices. For example, DST execution unit 1 monitors rebuilding messages 1and identifies generates storage status 1 based on the monitoredrebuilding messages 1. The rebuilding messages include at least one of alist slice requests, a list digest request, and a store rebuilt slicerequest. As another example, the DST execution unit 1 indicates afavorable slice storage status when rebuilding activity for the DSNaddress range over a time frame is less than a rebuilding thresholdlevel.

With each DST execution unit having determined associated slice storagestatus, the DST client module 34 receives, via the network 24, slicestorage status 1-n. Having received the slice storage status, the DSTclient module 34 determines to update the storage unit configuration ofone or more storage units of the set of storage units. The determiningmay be based on one or more of interpreting an error message, receivinga request, detecting suffer availability, detecting new hardwareavailability, detecting a software error, initiating a query, andreceiving a query response.

Having determined to update the storage unit configuration, the DSTclient module 34 determines whether to update the storage unitconfiguration based on the received slices storage status. For example,the DST client module 34 indicates to update the storage unitconfiguration when at least a threshold number of storage unitsassociated with favorable slice storage status for the DSN addressrange. When updating the storage unit configuration, the DST clientmodule 34 issues storage unit configuration updates 624 to one or moreof the DST execution units.

FIG. 47B is a flowchart illustrating an example of updating storage unitconfiguration information. The method begins or continues at step 626where a processing module of one or more processing modules of adispersed storage network (e.g., of a distributed storage and task (DST)client module, of a storage unit) determines, for each storage unit of aset of storage units, a slice storage status of a dispersed storagenetwork (DSN) address range. The determining includes one or more ofreceiving the status, initiating a query, and receiving a query responsethat includes the status. The determining may include determining, byeach storage unit, the storage status. For example, a storage unitindicates favorable slice storage status when a number of rebuildingmessages is less than a rebuilding message threshold level.

The method continues at step 628 where the processing module obtains theslice storage status of each storage unit of the set of storage units.The obtaining includes at least one of receiving, initiating a query,and receiving a query response. The method continues at step 630 wherethe processing module determines to update storage unit configuration ofone or more storage units of the set of storage units. The determiningincludes one or more of interpreting a maintenance schedule, receiving anew software version for uploading to one or more the storage units,detecting new hardware installed and a storage unit, and determining toperform a test.

The method continues at step 632 where the processing module determineswhether to update the storage unit configuration based on the storageunit configuration of at least some storage units of the set of storageunits. For example, the processing module indicates to update thestorage unit configuration when a threshold number of storage unitsassociated with favorable slice storage status for the DSN addressrange.

When updating the storage unit configuration, the method continues atstep 634 where the processing module issues a storage unit configurationupdate to one or more of the storage units of the set of storage units.The issuing includes one or more of initiating a test, indicatingutilizing new suffer version, issuing configuration information for newhardware, initiating a maintenance cycle, etc.

FIG. 48A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and at least two DST execution (EX) unit pools 1-2. Each DST executionunit pool includes a set of DST execution units 1-n. Each DST executionunit may be implemented utilizing the DST execution unit 36 of FIG. 1.The DST processing unit 16 includes a decentralized agreement module 636and a DST client module 34. The decentralized agreement module 636 maybe implemented utilizing the decentralized agreement module 350 of FIG.40A.

The DSN functions to migrate encoded data slices stored in the DSTexecution unit pool 1 to the DST execution unit pool 2 and to processwriting additional data as further encoded data slices to at least oneof the DST execution unit pool 1 and DST execution unit pool 2subsequent to initiation of the migration and prior to conclusion of themigration. For example, DST execution unit pool 2 is newly commissionedas a replacement to DST execution unit pool 1 which is at anend-of-life.

In an example of operation, the DST client module 34 determines toreplace the DST execution unit pool 1, where one or more first DSNaddress ranges are associated with the DST execution unit pool 1.Hereafter, the DST execution unit pool 1 may be interchangeably referredto as a first storage pool and the DST execution unit pool 2 may beinterchangeably referred to as a second storage pool. The determiningmay be based on one or more of interpreting a replacement schedule,receiving a request, and detecting an error.

Having determined to replace the first storage pool, the DST clientmodule 34 identifies a second storage pool to replace the first storagepool. Alternatively, the DST client module 34 identifies a second andthird storage pool to replace the first storage pool. The identifyingincludes one or more of detecting a new storage pool, receiving amanager input, identifying a storage pool associated with sufficientavailable capacity, and utilizing a decentralized agreement function toidentify a most favorable storage pool as the second storage pool. Forexample, the DST client module 34 issues a ranked scoring informationrequest 638 to the decentralized agreement module 636, where the request638 includes one or more of a DSN address of the first DSN addressranges, location weights of alternative storage pools, and identifiersof the alternative storage pools, and receives ranked scoringinformation 640.

Having identified the second storage pool, the DST client module 34issues migration messages 648 to the first and second storage pools toinitiate migration of encoded data slices from the first storage pool tothe second storage pool. The migration messages indicate slice namesassociated with at least one of all encoded data slices and encoded dataslices associated with slice names within a DSN address range. Forexample, the first storage pool issues, via the network 24, transferslice requests 646 to the second storage pool, where the transfer slicerequests 646 include at least some of the encoded data slices formigration.

When receiving a write slice request 642 issued by the DST client module34, by the first storage pool, prior to conclusion of the migration ofthe encoded data slices, one or more of the DST execution units of thefirst storage pool forwards a write slice request as a redirected writeslice request 644 to the corresponding DST execution units of the secondstorage pool for storage. For example, the DST client module 34 receivesdata for storage, encodes the data using a dispersed storage errorcoding function to produce at least one set of encoded data slices, andissues a set of write slice requests 642 to the first storage pool,where the write slice requests 642 includes the at least one set ofencoded data slices.

When the migration of the encoded data slices has concluded, the DSTclient module 34 disassociates the one or more first DSN address rangesfrom the first storage pool and associates the one or more first DSNaddress ranges with the second storage pool. Alternatively, or inaddition to, the DST client module 34 updates location weightsassociated with DST execution units of the DST execution unit pool 2,updates a DSN directory, and updates a dispersed hierarchical index.

FIG. 48B is a flowchart illustrating another example of migratingslices. The method begins or continues at step 650 where a processingmodule (e.g., of a distributed storage and task (DST) client module)determines to replace a first storage pool of a dispersed storagenetwork (DSN). The determining may include one or more of receiving arequest, detecting one or more storage errors, and interpreting areplacement schedule. The method continues at step 652 where theprocessing module identifies a second storage pool to replace the firststorage pool. The identifying includes at least one of detecting astorage pool, receiving a manager input, and identifying a storage poolassociated with available capacity greater than or equal to capacity ofthe first storage pool.

The method continues at step 654 where the processing module issuesmigration messages to at least one of the first storage pool and thesecond storage pool to initiate migration of encoded data slices fromthe first storage pool to the second storage pool. The issuing includesat least one of instructing the first storage pool to issue transferrequests to the second storage pool, instructing the second storage poolto request slices from the first storage pool, and notifying at leastone of the first and second storage pools of an open migration status.

When receiving a write request by the first storage pool, prior toconclusion of the migration, the method continues at step 656 where thefirst storage pool forwards the write slice request to the secondstorage pool. For example, a storage unit of the second storage poolreceives the write slice request, determines status of the migration,interprets the status to indicate that the migration status is open, andsends the write slice request to a corresponding storage unit of thesecond storage pool.

Upon conclusion of the migration, the method continues at step 658 wherethe processing module disassociates slice names previously associatedwith the first storage pool from the first storage pool and associatesthe slice names with the second storage pool. Alternatively, or inaddition to, the processing module zeros out a location weightassociated with a decentralized agreement function for the first storagepool, increases a location weight associated with the second storagepool, updates a DSN directory, and updates a DSN address to physicallocation table.

FIG. 49A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes a set of distributedstorage and task (DST) execution (EX) units 1-n and the network 24 ofFIG. 1. Each DST execution unit includes the processing module 84 ofFIG. 3 and a plurality of memories. Hereafter, a DST execution unit maybe interchangeably referred to as a storage unit. Each memory may beassociated with one or more properties. The properties may include oneor more of a manufacturer, a model number, a serial number range, themanufacturing date, hours of operation, expected service life, expectedremaining service life, hardware version, software version, firmwareversion, and a failure rate.

Each DST execution unit includes one or more subgroups of memories ofthe plurality of memories, where each subgroup is associated with aproperty class. Each property class includes one or more similarproperties. For example, DST execution unit 1 includes memories A-1through A-m that are associated with a property class A, memories C-1through C-m that are associated with a property class C, etc.

The DSN functions to store data as a plurality of sets of encoded dataslices and to rebalance storage of the encoded data slices within eachDST execution unit based on the property classes. In an example ofoperation, the processing module 84 of any storage unit identifies oneor more property classes of a plurality of memory devices associatedwith the storage unit. The identifying includes at least one ofinitiating a test, interpreting a test result, accessing a list,receiving, and interpreting DSN registry information.

Having identified the one or more property classes, the processingmodule 84 obtains a priority of usage level for each property class. Theobtaining includes at least one of determining, receiving, initiating aquery, and receiving a query response. For example, the processingmodule 84 determines a priority of usage level for the property class Aas a highest level when memory devices associated with the propertyclass A are associated with a favorable (e.g., lower than average)historical failure rate.

Having obtained the priority of usage level, the processing module 84identifies associations of encoded data slices of common data objectswith one or more property classes. For example, the processing module 84accesses a slice name to memory device identifier table. Havingidentified the associations, the processing module 84 obtainsconfiguration information 660 for the set of storage units that includesthe storage unit. The configuration information 660 includes one or moreof a list of property classes, DSN addresses associated with eachproperty class, a number of memory devices for each property class,known issues with a property class, and a priority of usage level foreach property class. For example, the processing module issues, via thenetwork 24, configuration information requests to other storage unitsand receives configuration information 660 from the other storage units.

Having obtained the configuration information 660, the processing module84 determines an updated configuration for the plurality of memorydevices of the storage unit based on one or more of the property ofusage levels, the associations, and the configuration information 660.For example, the processing module 84 determines the updatedconfiguration to result in utilizing a maximum number of memoriesassociated with different manufacturers for encoded data slicesassociated with a common data object to improve diversity-basedreliability. As another example, the processing module 84 determines theupdated configuration to move encoded data slices of the common dataobject from memories that are two years old to memories that are oneyear old to improve retrieval reliability. As yet another example, theprocessing module 84 determines the updated configuration to move theencoded data slices of the common data object away from memoriesassociated with a known faulty firmware version to other memories.Having determined the updated configuration, the processing module 84facilitates migration of one or more encoded data slices in accordancewith the updated configuration.

FIG. 49B is a flowchart illustrating another example of migratingslices. The method begins or continues at step 662 where a processingmodule (e.g., of a distributed storage and task (DST) execution unit)identifies one or more property classes of a plurality of memory devicesassociated with a storage unit of a set of storage units. Theidentifying includes at least one of initiating a test, interpreting atest result, accessing a list, interpreting dispersed storage network(DSN) registry information, and receiving.

For each property class, the method continues at step 664 where theprocessing module obtains a priority of usage level associated with theproperty class. The obtaining includes at least one of determining,receiving, initiating a query, and receiving a query response. Themethod continues at step 666 where the processing module identifiesassociations of encoded data slices of common data objects with one ormore property classes. The identifying includes at least one ofaccessing a DSN address to physical location table, accessing a slicelist, and utilizing a decentralized agreement function.

The method continues at step 668 where the processing module obtainsconfiguration information for the set of storage units. The obtainingincludes at least one of initiating a configuration information request,receiving a configuration information response, and accessing the DSNregistry information.

The method continues at step 670 where the processing module determinesan updated configuration for the association of the encoded data sliceswith the one or more property classes. The determining may be based onone or more of the property of usage levels, the associations, and theconfiguration information. For example, the processing module alignsstorage of slices in a common property class with other units of the setof storage units. As another example, the processing module facilitatesmoving slices from a property class with a known issue to anotherproperty class without an issue. As yet another example, the processingmodule moves slices from a memory of a property class with a leastfavorable usage level to another memory associated with a property classwith a more favorable usage level.

The method continues at step 672 where the processing module facilitatesmigration of one or more encoded data slices in accordance with theupdated configuration. The facilitating includes one or more of issuinga migration request, retrieving slices, stored slices, updating a slicename to physical location table, and updating location weightsassociated with memory devices of the storage unit in accordance withthe decentralized agreement function.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.,described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc., that mayuse the same or different reference numbers and, as such, the functions,steps, modules, etc., may be the same or similar functions, steps,modules, etc., or different ones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processing modules of one or more computing devices of a dispersed storage network (DSN), the method comprises: detecting a storage error associated with a memory device of a group of memory devices; identifying a sub-range of an DSN address range associated with the group of memory devices; facilitating rebuilding of the identified sub-range to produce rebuilt encoded data slices; updating location weights of the group of memory devices based on the detected storage error; and for each rebuilt encoded data slice, identifying a corresponding memory device of the group of memory devices for storage of the rebuilt encoded data slice utilizing a decentralized agreement function and the updated location weights; and storing the rebuilt encoded data slice in a corresponding identified memory device.
 2. The method of claim 1, wherein the detecting a storage error includes one or more of: receiving an error message, performing a memory device test, interpreting a memory device test result, detecting a corrupted slice, detecting a failed memory, or detecting a missing slice.
 3. The method of claim 1, wherein the identifying the sub-range of the DSN address range associated with a group of memory devices includes accessing a slice name to sub-range table using an identifier of the memory device.
 4. The method of claim 1, wherein the facilitating includes one or more of: scanning for missing slices across the sub-range, acquiring a decode threshold number of slices for each missing slice, or generating rebuilt slices from the acquired slices.
 5. The method of claim 1, wherein the updating location weights of the group of memory devices includes updating a location weight for a failed memory device to zero and raising location weights for remaining memory devices of a group of memory devices in a total amount equivalent to a previous location weight for the failed memory device.
 6. The method of claim 1, wherein the identifying a corresponding memory device of the group of memory devices for storage of the rebuilt encoded data slice utilizing the decentralized agreement function and the updated location weights includes performing the decentralized agreement function for each of the memory devices using updated location weights, a slice name of the rebuilt encoded data slice, and a memory group identifier to produce ranked scoring information.
 7. The method of claim 6 further comprises identifying a corresponding memory device associated with a highest score of the ranked scoring information.
 8. The method of claim 1, wherein the storing the rebuilt encoded data slice in the corresponding identified memory device includes sending the encoded data slice to the identified corresponding memory device for each rebuilt encoded data slice.
 9. A computing device of a group of computing devices of a dispersed storage network (DSN), the computing device comprises: a network interface; a local memory; and a processing module operably coupled to the local memory and the network interface, the processing module configured to: when accessing an encoded data slice: receive a slice access request that includes a slice name; identify a sub-range of a DSN address range based on the slice name; identify a memory device of a group of memory devices associated with the sub-range utilizing a decentralized agreement function based on the slice name; facilitate a slice access request with the identified memory device; and when rebuilding an encoded data slice: detect a storage error associated with a memory device of the group of memory devices; identify the sub-range of the DSN address range associated with a group of memory devices; facilitate rebuilding of the identified sub-range to produce rebuilt encoded data slices; update location weights of the group of memory devices based on the detected storage error; for each rebuilt encoded data slice, identify a corresponding memory device of the group of memory devices for storage of the rebuilt encoded data slice utilizing the decentralized agreement function and the updated location weights; and store the rebuilt encoded data slice in the corresponding identified memory device.
 10. The computing device of claim 9, wherein the identify a memory device of a group of memory devices associated with the sub-range utilizing a decentralized agreement function based on the slice name includes performing the decentralized agreement function to produce scores for each of the memory devices of a group of memory devices using one or more of: location weights of each memory device, the slice name, or a memory group identifier.
 11. The computing device of claim 9, wherein the facilitate includes one or more of: scanning for missing slices across the sub-range, acquiring a decode threshold number of slices for each missing slice, or generating rebuilt slices from the acquired slices.
 12. The computing device of claim 9, wherein the update location weights of the group of memory devices includes updating a location weight for a failed memory device to zero and raising location weights for remaining memory devices of a group of memory devices in a total amount equivalent to a previous location weight for the failed memory device.
 13. The computing device of claim 9, wherein the identify a corresponding memory device of the group of memory devices for storage of the rebuilt encoded data slice utilizing the decentralized agreement function and the updated location weights includes performing the decentralized agreement function for each of the memory devices using updated location weights, a slice name of the rebuilt encoded data slice, and a memory group identifier to produce ranked scoring information.
 14. The computing device of claim 13 further comprises identifying a corresponding memory device associated with a highest score of the ranked scoring information.
 15. The computing device of claim 9, wherein the updating location weights of the group of memory devices includes updating a location weight for a failed memory device to zero and raising location weights for remaining memory devices of the group of memory devices in a total amount equivalent to a previous location weight for the failed memory device.
 16. A method for execution by one or more processing modules of one or more computing devices of a dispersed storage network (DSN), the method comprises: when accessing an encoded data slice: receiving a slice access request that includes a slice name; identifying a sub-range of a DSN address range based on the slice name; identifying a memory device of a group of memory devices associated with the sub-range utilizing a decentralized agreement function based on the slice name; facilitating a slice access request with the identified memory device; and when rebuilding an encoded data slice: detecting a storage error associated with a memory device of the group of memory devices; identifying the sub-range of the DSN address range associated with a group of memory devices; facilitating rebuilding of the identified sub-range to produce rebuilt encoded data slices; updating location weights of the group of memory devices based on the detected storage error; for each rebuilt encoded data slice, identifying a corresponding memory device of the group of memory devices for storage of the rebuilt encoded data slice utilizing the decentralized agreement function and the updated location weights; and storing the rebuilt encoded data slice in a corresponding identified memory device.
 17. The method of claim 16, wherein the identifying a sub-range of a DSN address range includes any of: accessing a slice name to sub-range table or performing a deterministic function on the slice name to produce the sub-range.
 18. The method of claim 16, wherein the identifying a memory device of a group of memory devices associated with the sub-range utilizing a decentralized agreement function based on the slice name includes performing the decentralized agreement function to produce scores for each of the memory devices of a group of memory devices using one or more of: location weights of each memory device, the slice name, or a memory group identifier.
 19. The method of claim 16, wherein the identify a corresponding memory device of the group of memory devices for storage of the rebuilt encoded data slice utilizing the decentralized agreement function and the updated location weights includes performing the decentralized agreement function for each of the memory devices using updated location weights, a slice name of the rebuilt encoded data slice, and a memory group identifier to produce ranked scoring information.
 20. The method of claim 16, wherein the updating location weights of the group of memory devices includes updating a location weight for a failed memory device to zero and raising location weights for remaining memory devices of the group of memory devices in a total amount equivalent to a previous location weight for the failed memory device. 